Towards uniform case-identification criteria in observational studies on peripheral arterial disease: A scoping review

OBJECTIVE

The diagnosis peripheral arterial disease (PAD) is commonly applied for symptoms related to atherosclerotic obstructions in the lower extremity, though its clinical manifestations range from an abnormal Ankle Brachial Index to critical limb ischemia. Subsequently, management and prognosis of PAD vary widely with the disease stage. A critical aspect is how this variation is addressed in administrative databases-based studies that rely on diagnosis codes for case identification. The objective of this scoping review is to inventory the identification strategies used in studies on PAD that rely on administrative databases, to map the pros and cons of the ICD codes applied, and propose a first outline for a consensus framework for case identification in administrative databases.

METHODS

Registry-based reports published between 2010 to 2021 were identified through a systematic PubMed search. Studies were sub-categorized on the basis of the expressed study focus: claudication, critical limb ischemia, or general peripheral arterial disease and the ICD code(s) applied for case identification mapped.

RESULTS

Ninety studies were identified, of which thirty-six (40%) did not specify the grade of PAD studied. Forty-nine (54%) articles specified PAD grade studied. Five (6%) articles specified different PAD subgroups in methods and baseline demographics, but not in further analyses. Mapping of the ICD codes applied for case identification for studies that specified the PAD grade studied indicated a remarkable heterogeneity, overlap, and inconsistency.

CONCLUSION

A large proportion of registry-based studies on PAD fails to define the study focus. In addition, inconsistent strategies are used for PAD case-identification in studies that report a focus. These findings challenge study validity, and interfere with inter-study comparison. This scoping review provides a first initiative for a consensus framework for standardized case selection in administrative studies on PAD. It is anticipated that more uniform coding will improve study validity, and facilitate inter-study comparisons.

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.

ICD2Vec: Mathematical representation of diseases

BACKGROUND

The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes connote direct human-defined relationships among diseases in a hierarchical tree structure. Representing the ICD codes as mathematical vectors helps to capture nonlinear relationships in medical ontologies across diseases.

METHODS

We propose a universally applicable framework called "ICD2Vec" designed to provide mathematical representations of diseases by encoding corresponding information. First, we present the arithmetical and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we investigated the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we propose a new risk score called IRIS, derived from ICD2Vec, and demonstrate its clinical utility with large cohorts from the UK and South Korea.

RESULTS

Semantic compositionality was qualitatively confirmed between descriptions of symptoms and ICD2Vec. For example, the most diseases most similar to COVID-19 were found to be the common cold (ICD-10: J00), unspecified viral hemorrhagic fever (ICD-10: A99), and smallpox (ICD-10: B03). We show the significant associations between the cosine similarities derived from ICD2Vec and the biological relationships using disease-to-disease pairs. Furthermore, we observed significant adjusted hazard ratios (HR) and area under the receiver operating characteristics (AUROC) between IRIS and risks for eight diseases. For instance, the higher IRIS for coronary artery disease (CAD) can be the higher probability for the incidence of CAD (HR: 2.15 [95% CI 2.02-2.28] and AUROC: 0.587 [95% CI 0.583-0.591]). We identified individuals at substantially increased risk of CAD using IRIS and 10-year atherosclerotic cardiovascular disease risk (adjusted HR, 4.26, 95% CI, 3.59-5.05).

CONCLUSIONS

ICD2Vec, a proposed universal framework for converting qualitatively measured ICD codes into quantitative vectors containing semantic relationships between diseases, exhibited a significant correlation with actual biological significance. In addition, the IRIS was a significant predictor of major diseases in a prospective study using two large-scale Biobank EHR datasets. Based on this clinical validity and utility evidence, we suggest that publicly available ICD2Vec can be used in diverse research and clinical practices and has important clinical implications.

An Increase in the Use of ICD-10 Z-Codes for Social Risks and Social Needs: 2015 to 2019

Attention to 10th Revision of the International Classification of Disease (ICD-10)-CM Z-codes as a mechanism for capturing social risk has grown over the years. However, it remains unclear whether the use of Z-codes has changed over time. This study sought to examine the trends in Z-code use between their introduction in 2015 and the end of 2019 across 2 markedly different states. Using the Healthcare Cost and Utilization Project, all emergency department visits or hospitalizations at short-term general hospitals in Florida and Maryland from 2015 Q4 through 2019 were identified. This study focused on a subset of the Z-codes that are intended to capture social risk to identify the percentage of encounters with a Z-code, percentage of facilities using Z-codes, and facility median of number of encounters with a Z-code per 1000 encounters across quarters, states, and care settings. In total, 495,212 (0.84%) of 58,993,625 encounters had a Z-code. Despite Florida's higher area deprivation, Z-codes were less frequently used and increasing more slowly than when compared with those in Maryland. There were 2.1 times the use of Z-codes in Maryland than in Florida at the encounter level. This difference was also seen when evaluating the median number of encounters with a Z-code, per 1000 encounters (12.1 vs. 3.4). Z-codes were more commonly used at major teaching facilities, and for patients who were uninsured or on Medicaid. The use of ICD-10-CM Z-codes has increased over time, with this increase occurring at nearly all short-term general hospitals. Their use was higher in Maryland than in Florida and among major teaching facilities.

Validation of the International Classification of Diseases, Tenth Revision Code for the National Institutes of Health Stroke Scale Score

BACKGROUND

Administrative data can be useful for stroke research but have historically lacked data on stroke severity. Hospitals increasingly report the National Institutes of Health Stroke Scale (NIHSS) score using an International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code, but this code's validity remains unclear.

METHODS

We examined the concordance of ICD-10 NIHSS scores versus NIHSS scores recorded in CAESAR (Cornell Acute Stroke Academic Registry). We included all patients with acute ischemic stroke from October 1, 2015, when US hospitals transitioned to ICD-10, through 2018, the latest year in our registry. The NIHSS score (range, 0-42) recorded in our registry served as the reference gold standard. ICD-10 NIHSS scores were derived from hospital discharge diagnosis code R29.7xx, with the latter 2 digits representing the NIHSS score. Multiple logistic regression was used to explore factors associated with availability of ICD-10 NIHSS scores. We used ANOVA to examine the proportion of variation (R2) in the true (registry) NIHSS score that was explained by the ICD-10 NIHSS score.

RESULTS

Among 1357 patients, 395 (29.1%) had an ICD-10 NIHSS score recorded. This proportion increased from 0% in 2015 to 46.5% in 2018. In a logistic regression model, only higher registry NIHSS score (odds ratio per point, 1.05 [95% CI, 1.03-1.07]) and cardioembolic stroke (odds ratio, 1.4 [95% CI, 1.0-2.0]) were associated with availability of the ICD-10 NIHSS score. In an ANOVA model, the ICD-10 NIHSS score explained almost all the variation in the registry NIHSS score (R2=0.88). Fewer than 10% of patients had a large discordance (≥4 points) between their ICD-10 and registry NIHSS scores.

CONCLUSIONS

When present, ICD-10 codes representing NIHSS scores had excellent agreement with NIHSS scores recorded in our stroke registry. However, ICD-10 NIHSS scores were often missing, especially in less severe strokes, limiting the reliability of these codes for risk adjustment.

Investigator-Determined Categories for Fever of Unknown Origin (FUO) Compared With International Classification of Diseases-10 Classification of Illness: A Systematic Review and Meta-analysis With a Proposal for Revised FUO Classification

Background

Classifying fever of unknown origin (FUO) into categorical etiologies (ie, infections, noninfectious inflammatory, oncologic, miscellaneous, and undiagnosed disorders) remains unstandardized and subject to discrepancies. As some disease classifications change, a systematic review of studies would help physicians anticipate the frequency of illness types they may encounter that could influence care.

Methods

We systematically reviewed prospective FUO studies published across the Medline (PubMed), Embase, Scopus, and Web of Science databases from January 1, 1997, to July 31, 2022. We performed a meta-analysis to estimate associated pooled proportions between the investigator-determined choice of disease category and those determined by the International Classification of Diseases, 10th edition (ICD-10), methodology.

Results

The proportion of patients with a difference between the investigator and ICD-10-adjusted noninfectious inflammatory disorder category was 1.2% (95% CI, 0.005-0.021; P < .001), and the proportion was similar for the miscellaneous category at 1.5% (95% CI, 0.007-0.025; P < .001). The miscellaneous and noninfectious inflammatory disorders categories demonstrated significant across-study heterogeneity in the proportions of patients changing categories, with 52.7% (P = .007) and 51.0% (P = .010) I² , respectively.

Conclusions

Adjusting FUO-associated diagnoses by ICD-10 methodology was associated with a statistically significant risk of over- or underestimation of disease category frequency approximation when using a 5 FUO category system. An FUO diagnostic classification system that better reflects mechanistic understanding would assist future research and enhance comparability across heterogenous populations and different geographic regions. We propose an updated FUO classification scheme that streamlines categorizations, aligns with the current understanding of disease mechanisms, and should facilitate empirical decisions, if necessary.

Deriving International Classification of Diseases, 9th and 10th revision, codes for identifying and following up patients with diabetic lower extremity ulcers

Objective

Administrative claims data offer a rich data source for clinical research. However, its application to the study of diabetic lower extremity ulceration is lacking. Our objective was to create a widely applicable framework by which investigators might derive and refine the International Classification of Diseases, 9th and 10th revision (ICD-9 and ICD-10, respectively) codes for use in identifying diabetic, lower extremity ulceration.

Methods

We created a seven-step process to derive and refine the ICD-9 and ICD-10 coding lists to identify diabetic lower extremity ulcers. This process begins by defining the research question and the initial identification of a list of ICD-9 and ICD-10 codes to define the exposures or outcomes of interest. These codes are then applied to claims data, and the rates of clinical events are examined for consistency with prior research and changes across the ICD-9 to ICD-10 transition. The ICD-9 and ICD-10 codes are then cross referenced with each other to further refine the lists.

Results

Using this method, we started with 8 ICD-9 and 43 ICD-10 codes used to identify lower extremity ulcers in patients with known diabetes and peripheral arterial disease and examined the association of ulceration with lower extremity amputation. After refinement, we had 45 ICD-9 codes and 304 ICD-10 codes. We then grouped the codes into eight clinical exposure groups and examined the rates of amputation as a rudimentary test of validity. We found that the rate of lower extremity amputation correlated with the severity of lower extremity ulceration.

Conclusions

We identified 45 ICD-9 and 304 ICD-10 ulcer codes, which identified patients at risk of amputation from diabetes and peripheral artery disease. Although further validation at the medical record level is required, these codes can be used for claims-based risk stratification for long-term outcomes assessment in the treatment of patients at risk of limb loss.

Accuracy of International Classification of Diseases, 10th Revision Codes for Identifying Sepsis: A Systematic Review and Meta-Analysis

Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis. This systematic review and meta-analysis examines the accuracy of International Classification of Diseases, 10th revision (ICD-10), codes for identifying sepsis in adult and pediatric patients.

DATA SOURCES

We searched MEDLINE, EMBASE, Web of Science, CENTRAL, Epistemonikos, and McMaster Superfilters from inception to September 7, 2021.

STUDY SELECTION

We included studies that validated the accuracy of sepsis ICD-10 codes against any reference standard.

DATA EXTRACTION

Three authors, working in duplicate, independently extracted data. We conducted meta-analysis using a random effects model to pool sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated individual study risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool and assessed certainty in pooled diagnostic effect measures using the Grading of Recommendations Assessment, Development, and Evaluation framework.

DATA SYNTHESIS

Thirteen eligible studies were included in the qualitative synthesis and the meta-analysis. Eleven studies used manual chart review as the reference standard, and four studies used registry databases. Only one study evaluated pediatric patients exclusively. Compared with the reference standard of detailed chart review and/or registry databases, the pooled sensitivity for sepsis ICD-10 codes was 35% (95% CI, 22-48, low certainty), whereas the pooled specificity was 98% (95% CI: 98-99, low certainty). The PPV for ICD-10 codes ranged from 9.8% to 100% (median, 72.0%; interquartile range [IQR], 50.0-84.7%). NPV ranged from 54.7% to 99.1% (median, 95.9%; interquartile range, 85.5-98.3%).

CONCLUSIONS

Sepsis is undercoded in administrative databases. Future research is needed to explore if greater consistency in ICD-10 code definitions and enhanced quality measures for ICD-10 coders can improve the coding accuracy of sepsis in large databases.

Characterizing the Accuracy of International Classification of Diseases, Tenth Revision Administrative Claims for Aortic Valve Disease

BACKGROUND

Administrative claims for aortic stenosis (AS) regurgitation may be useful, but their accuracy and ability to identify individuals at risk for valve-related outcomes have not been well characterized.

METHODS

Using echocardiographic (transthoracic echocardiogram [TTE]) reports linked to US Medicare claims, 2017 to 2018, the performance of candidate International Classification of Diseases, Tenth Revision claims to ascertain AS/aortic regurgitation was evaluated. The optimal performing algorithm was tested against outcomes at 1-year after TTE in a separate 100% sample of US Medicare claims, 2017 to 2019.

RESULTS

Of those included in the derivation (N=5497, mean age 74.4±11.0 years, 49.7% female), any AS or aortic regurgitation was present in 24% and 38.8%, respectively. The sensitivity and specificity of International Classification of Diseases, Tenth Revision code I35.0 for identification of any AS was 53.1% and 94.8%, respectively. Among those with an I35.0 code, 40.3% had severe AS. Claims were unable to distinguish disease severity (ie, severe versus nonsevere) or subtype (eg, bicuspid or rheumatic AS), and were insensitive and nonspecific for aortic regurgitation of any severity. Among all beneficiaries who received a TTE (N=4 033 844), adjusting for age, sex, and 27 comorbidities, those with an I35.0 code had a higher adjusted risk of all-cause mortality (adjusted hazard ratio, 1.33 [95% CI, 1.31-1.34]), heart failure hospitalization (adjusted hazard ratio, 1.37 [95% CI, 1.34-1.41]), and aortic valve replacement (adjusted hazard ratio, 34.96 [95% CI, 33.74-36.22]).

CONCLUSIONS

Among US Medicare beneficiaries receiving a TTE, International Classification of Diseases, Tenth Revision claims, though identifying a population at significant greater risk of valve-related outcomes, failed to identify nearly half of individuals with AS and were unable to distinguish disease severity or subtype. These results argue against the widespread use of International Classification of Diseases, Tenth Revision claims to screen for patients with AS and suggests the need for improved coding algorithms and alternative systems to extract TTE data for quality improvement and hospital benchmarking.

Trans Expertise and the Revision and Translation of ICD-11: An Analysis of Available French Data from a Trans Perspective

Transgender and gender diverse (TGD) individuals' depsychopathologization in the eleventh revision of the International Classification of Diseases (ICD-11) faces systemic discriminations built-in epistemic pipelines. Based on an analysis of unexploited data from ICD-11 and the French translation process, this article addresses power issues in participatory research and systemic discrimination within a socio-cultural context. We used a peer-driven participatory approach to conduct qualitative analyses of the French version of the ICD based on contributions from 72 TGD participants in the French study for ICD-11. The results highlight a major incongruence between participants' propositions and the final official translation. Alternative terms were proposed and discussed by participants in regard to usage and concepts, but also encompassed participation and perceived futility of maintaining pathologization. We found discrepancies in the French publication and translation processes, respectively on gender categorization and back translation. These results question the relevance and implementation of ICD-11 for TGD communities and highlight failures at all three stages of the official French translation. Power issues have an impact on knowledge production and, while mechanisms vary, all relate to epistemic injustice. Involving TGD communities in all stages of medical knowledge production processes would reduce transphobic biases. Individuals with personal stakes involved in politicized research areas appear all the more necessary today.

Accuracy of International Classification of Diseases-10 Codes for Cirrhosis and Portal Hypertensive Complications

BACKGROUND AND AIMS

International Classification of Diseases (ICD)-10 codes may correspond to cirrhosis diagnosis. However, these codes have not been as well studied as ICD-9 codes. We aimed to evaluate the positive predictive value (PPV) and specificity of ICD-10 codes for cirrhosis.

METHODS

We conducted a single-center retrospective study of patients in Michigan Medicine (Ann Arbor, MI, USA). We evaluated patients with at least one of 28 ICD-10 codes for cirrhosis and randomly selected controls for the presence of cirrhosis and/or portal hypertensive complications.

RESULTS

Among 1317 patients with at least one ICD-10 code consistent with cirrhosis and/or portal hypertension, 796 had confirmed cirrhosis. After excluding ICD-10 codes found in < 10 patients, we evaluated the PPV of the 19 remaining codes. Of these, 15 had a high PPV for cirrhosis (> 80%), including codes for cirrhosis itself, gastroesophageal varices, hepatic encephalopathy, and other portal hypertensive complications. Specificity of ICD codes for cirrhosis for these 15 codes was high (> 94% for all). PPV and specificity were high across liver disease etiologies. Among patients without portal hypertension, PPVs of ICD-10 codes for cirrhosis were lower but still > 80% for the most common codes. PPVs of most codes for portal hypertensive complications were > 70%. Defining cirrhosis based on the presence of any of the 15 codes resulted in a PPV of 86% and by two different codes, a PPV 94%.

CONCLUSIONS

ICD-10 codes for cirrhosis can accurately identify patients with cirrhosis with or without portal hypertensive complications.

'If it can't be coded, it doesn't exist'. A historical-philosophical analysis of the new ICD-11 classification of chronic pain

Chronic pain entails a large burden of disease and high social costs, but is seldom 'in the picture' and barely understood. Until recently, it was not systematically classified but instead viewed as a symptom or sign. In the new International Classification of Diseases, (ICD)-11, to be implemented in 2022, 'chronic' pain is now classified as a separate disease category and, to a certain extent, approached as a 'disease in its own right'. Reasons that have been given for this are not based so much on new scientific insights, but are rather of pragmatic nature. To explore the background of these recent changes in definition and classification of chronic pain, this paper provides a historical-philosophical analysis. By sketching a brief history of how pain experts have been working on the definition and taxonomy since the 1970s, we demonstrate the various social and practical functions that underlie the new ICD-11 classification of chronic pain. Building on this historical-empirical basis, we discuss philosophical issues regarding defining and classifying chronic pain, in particular performativity and pragmatism, and discuss their implications for the broader philosophical debate on health and disease.

Effectiveness of the Use of Standardized Vocabularies on Epilepsy Patient Cohort Generation

Objectives

This study investigated the effectiveness of using standardized vocabularies to generate epilepsy patient cohorts with local medical codes, SNOMED Clinical Terms (SNOMED CT), and International Classification of Diseases tenth revision (ICD-10)/Korean Classification of Diseases-7 (KCD-7).

Methods

We compared the granularity between SNOMED CT and ICD-10 for epilepsy by counting the number of SNOMED CT concepts mapped to one ICD-10 code. Next, we created epilepsy patient cohorts by selecting all patients who had at least one code included in the concept sets defined using each vocabulary. We set patient cohorts generated by local codes as the reference to evaluate the patient cohorts generated using SNOMED CT and ICD-10/KCD-7. We compared the number of patients, the prevalence of epilepsy, and the age distribution between patient cohorts by year.

Results

In terms of the cohort size, the match rate with the reference cohort was approximately 99.2% for SNOMED CT and 94.0% for ICD-10/KDC7. From 2010 to 2019, the mean prevalence of epilepsy defined using the local codes, SNOMED CT, and ICD-10/KCD-7 was 0.889%, 0.891% and 0.923%, respectively. The age distribution of epilepsy patients showed no significant difference between the cohorts defined using local codes or SNOMED CT, but the ICD-9/KCD-7-generated cohort showed a substantial gap in the age distribution of patients with epilepsy compared to the cohort generated using the local codes.

Conclusions

The number and age distribution of patients were substantially different from the reference when we used ICD-10/KCD-7 codes, but not when we used SNOMED CT concepts. Therefore, SNOMED CT is more suitable for representing clinical ideas and conducting clinical studies than ICD-10/KCD-7.

Automatic International Classification of Diseases Coding System: Deep Contextualized Language Model With Rule-Based Approaches

BACKGROUND

The tenth revision of the International Classification of Diseases (ICD-10) is widely used for epidemiological research and health management. The clinical modification (CM) and procedure coding system (PCS) of ICD-10 were developed to describe more clinical details with increasing diagnosis and procedure codes and applied in disease-related groups for reimbursement. The expansion of codes made the coding time-consuming and less accurate. The state-of-the-art model using deep contextual word embeddings was used for automatic multilabel text classification of ICD-10. In addition to input discharge diagnoses (DD), the performance can be improved by appropriate preprocessing methods for the text from other document types, such as medical history, comorbidity and complication, surgical method, and special examination.

OBJECTIVE

This study aims to establish a contextual language model with rule-based preprocessing methods to develop the model for ICD-10 multilabel classification.

METHODS

We retrieved electronic health records from a medical center. We first compared different word embedding methods. Second, we compared the preprocessing methods using the best-performing embeddings. We compared biomedical bidirectional encoder representations from transformers (BioBERT), clinical generalized autoregressive pretraining for language understanding (Clinical XLNet), label tree-based attention-aware deep model for high-performance extreme multilabel text classification (AttentionXLM), and word-to-vector (Word2Vec) to predict ICD-10-CM. To compare different preprocessing methods for ICD-10-CM, we included DD, medical history, and comorbidity and complication as inputs. We compared the performance of ICD-10-CM prediction using different preprocesses, including definition training, external cause code removal, number conversion, and combination code filtering. For the ICD-10 PCS, the model was trained using different combinations of DD, surgical method, and key words of special examination. The micro F₁ score and the micro area under the receiver operating characteristic curve were used to compare the model's performance with that of different preprocessing methods.

RESULTS

BioBERT had an F₁ score of 0.701 and outperformed other models such as Clinical XLNet, AttentionXLM, and Word2Vec. For the ICD-10-CM, the model had an F₁ score that significantly increased from 0.749 (95% CI 0.744-0.753) to 0.769 (95% CI 0.764-0.773) with the ICD-10 definition training, external cause code removal, number conversion, and combination code filter. For the ICD-10-PCS, the model had an F₁ score that significantly increased from 0.670 (95% CI 0.663-0.678) to 0.726 (95% CI 0.719-0.732) with a combination of discharge diagnoses, surgical methods, and key words of special examination. With our preprocessing methods, the model had the highest area under the receiver operating characteristic curve of 0.853 (95% CI 0.849-0.855) and 0.831 (95% CI 0.827-0.834) for ICD-10-CM and ICD-10-PCS, respectively.

CONCLUSIONS

The performance of our model with the pretrained contextualized language model and rule-based preprocessing method is better than that of the state-of-the-art model for ICD-10-CM or ICD-10-PCS. This study highlights the importance of rule-based preprocessing methods based on coder coding rules.

Chiropractic Services and Diagnoses for Low Back Pain in 3 U.S. Department of Defense Military Treatment Facilities: A Secondary Analysis of a Pragmatic Clinical Trial

OBJECTIVE

The purpose of this study was to describe the diagnoses and chiropractic services performed by doctors of chiropractic operating within 3 military treatment facilities for patients with low back pain (LBP).

METHODS

This was a descriptive secondary analysis of a pragmatic clinical trial comparing usual medical care (UMC) plus chiropractic care to UMC alone for U.S. active-duty military personnel with LBP. Participants who were allocated to receive UMC plus 6 weeks of chiropractic care and who attended at least 1 chiropractic visit (n = 350; 1547 unique visits) were included in this analysis. International Classification of Diseases and Current Procedural Terminology codes were transcribed from chiropractic treatment paper forms. The number of participants receiving each diagnosis and service and the number of each service on unique visits was tabulated. Low back pain and co-occurring diagnoses were grouped into neuropathic, nociceptive, bone and/or joint, general pain, and nonallopathic lesions categories. Services were categorized as evaluation, active interventions, and passive interventions.

RESULTS

The most reported pain diagnoses were lumbalgia (66.1%) and thoracic pain (6.6%). Most reported neuropathic pain diagnoses were sciatica (4.9%) and lumbosacral neuritis or radiculitis (2.9%). For the nociceptive pain, low back sprain and/or strain (15.8%) and lumbar facet syndrome (9.2%) were most common. Most reported diagnoses in the bone and/or joint category were intervertebral disc degeneration (8.6%) and spondylosis (6.0%). Tobacco use disorder (5.7%) was the most common in the other category. Chiropractic care was compromised of passive interventions (94%), with spinal manipulative therapy being the most common, active interventions (77%), with therapeutic exercise being most common, and a combination of passive and active interventions (72%).

CONCLUSION

For the sample in this study, doctors of chiropractic within 3 military treatment facilities diagnosed, managed, and provided clinical evaluations for a range of LBP conditions. Although spinal manipulation was the most commonly used modality, chiropractic care included a multimodal approach, comprising of both active and passive interventions a majority of the time.

[Obsessive-compulsive and related disorders according to ICD-11 and DSM-5 : With brief consideration of the new obsessive-compulsive disorders olfactory reference disorder and hypochondriasis]

This article summarizes the current state of research with respect to the new obsessive-compulsive and related disorders (OCRD) grouping according to the International Statistical Classification of Diseases and Related Health Problems (ICD-11) and the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The ICD-11 grouping of OCRD is based on common clinical features, such as repetitive undesired thoughts and repetitive behavior and is supported by the literature and empirical data from the fields of imaging and genetics. The disorders in this grouping in ICD-11 include obsessive-compulsive disorder, pathological hoarding, body dysmorphic disorder, trichotillomania, excoriation disorder, and as new disorders differing to DSM‑5, hypochondriasis, olfactory reference disorder and Tourette syndrome. The aim of the OCRD grouping is an improvement of the diagnostics and appropriate treatment strategies as well as a further stimulation of research. The new disorders olfactory reference disorder and hypochondriasis are presented and discussed.

Improving identification of crash injuries: Statewide integration of hospital discharge and crash report data

OBJECTIVE

The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type.

METHODS

We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days.

RESULTS

In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists.

CONCLUSIONS

Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.

Emerging experience with selected new categories in the ICD-11: complex PTSD, prolonged grief disorder, gaming disorder, and compulsive sexual behaviour disorder

Among the important changes in the ICD-11 is the addition of 21 new mental disorders. New categories are typically proposed to: a) improve the usefulness of morbidity statistics; b) facilitate recognition of a clinically important but poorly classified mental disorder in order to provide appropriate management; and c) stimulate research into more effective treatments. Given the major implications for the field and for World Health Organization (WHO) member states, it is important to examine the impact of these new categories during the early phase of the ICD-11 implementation. This paper focuses on four disorders: complex post-traumatic stress disorder, prolonged grief disorder, gaming disorder, and compulsive sexual behaviour disorder. These categories were selected because they have been the focus of considerable activity and/or controversy and because their inclusion in the ICD-11 represents a different decision than was made for the DSM-5. The lead authors invited experts on each of these disorders to provide insight into why it was considered important to add it to the ICD-11, implications for care of not having that diagnostic category, important controversies about adding the disorder, and a review of the evidence generated and other developments related to the category since the WHO signaled its intention to include it in the ICD-11. Each of the four diagnostic categories appears to describe a population with clinically important and distinctive features that had previously gone unrecognized as well as specific treatment needs that would otherwise likely go unmet. The introduction of these categories in the ICD-11 has been followed by a substantial expansion of research in each area, which has generally supported their validity and utility, and by a significant increase in the availability of appropriate services.

Classification of stillbirth by the International Classification of Diseases for Perinatal Mortality using a sequential approach: A 20-year retrospective study from Thailand

AIM

The International Classification of Diseases for Perinatal Mortality (ICD-PM) is a system for recording causes of perinatal death. In this system, placental pathology is considered a "maternal condition" and this category does not cover the spectrum of placental pathology that can impact on perinatal death. The aim of the study was to apply a wider spectrum of placental pathology as a separate parameter for classifying death in the ICD-PM.

METHODS

All autopsy reports at a single institution over a 20-year period (2001-2020) were reviewed. Causes of stillbirth were analyzed in a sequential manner: step 1, clinical history and laboratory results; step 2, placenta; and step 3, autopsy; and classified at each step according to the ICD-PM.

RESULTS

The review identified 330 cases, including 126 antepartum and 204 intrapartum deaths. Step 1 identified a cause in 176 (86%) intrapartum deaths and 64 (51%) antepartum deaths. The addition of placental pathology (step 2) changed the cause of death in 12% of cases, with causes now identified in 190 (93%) intrapartum and 89 (71%) antepartum deaths. Adding step 3 did not identify any additional causes of death.

CONCLUSION

The accuracy of the ICD-PM classification is dependent on the data available. Placental pathology made a significant difference in assigning causes of death in our series, stressing the importance of placental examination. Determination of the cause of death based on clinical history and laboratory data alone may be inaccurate, and less useful for comparative studies and planning prenatal care.

Hospitalisation with injection-related infections: Validation of diagnostic codes to monitor admission trends at a tertiary care hospital in Melbourne, Australia

INTRODUCTION

Injection-related infections (IRI) cause morbidity and mortality in people who inject drugs. Hospital administrative datasets can be used to describe hospitalisation trends, but there are no validated algorithms to identify injecting drug use and IRIs. We aimed to validate International Classification of Diseases (ICD) codes to identify admissions with IRIs and use these codes to describe IRIs within our hospital.

METHODS

We developed a candidate set of ICD codes to identify current injecting drug use and IRI and extracted admissions satisfying both criteria. We then used manual chart review data from 1 January 2017 to 30 April 2019 to evaluate the performance of these codes and refine our algorithm by selecting codes with a high-positive predictive value (PPV). We used the refined algorithm to describe trends and outcomes of people who inject drugs with an IRI at Alfred Hospital, Melbourne from 2008 to 2020.

RESULTS

Current injecting drug use was best predicted by opioid-related disorders (F11), 80% (95% confidence interval [CI] 74-85%), and other stimulant-related disorders (F15), 82% (95% CI 70-90%). All PPVs were ≥67% to identify specific IRIs, and ≥84% for identifying any IRI. Using these codes over 12 years, IRIs increased from 138 to 249 per 100 000 admissions, and skin and soft tissues infections (SSTI) were the most common (797/1751, 46%).

DISCUSSION AND CONCLUSION

Validated ICD-based algorithms can inform passive surveillance systems. Strategies to reduce hospitalisation with IRIs should be supported by early intervention and prevention, particularly for SSTIs which may represent delayed access to care.

Cesarean delivery on maternal request in the United States from 1999 to 2015

BACKGROUND

The rate of cesarean delivery has increased in the United States over the last several decades. However, the rate of cesarean delivery on maternal request remains undetermined, and recent data on cesarean delivery on maternal request are lacking.

OBJECTIVE

This study aimed to describe the prevalence and temporal trends of cesarean delivery on maternal request in the United States and characterize the population of women who elect to undergo a cesarean delivery in the absence of fetal or maternal indications. Maternal outcomes between women who delivered by cesarean delivery on maternal request and those who did not were compared.

STUDY DESIGN

A retrospective cohort study was conducted using data from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample from 1999 to 2015. An algorithm based on International Classification of Diseases, Ninth Revision codes was created to identify patients who underwent a primary elective cesarean delivery in the absence of fetal or maternal indications. Maternal characteristics and outcomes between women who delivered by cesarean delivery on maternal request and those who did not were compared using descriptive and logistic regression analyses.

RESULTS

Of the 13,698,835 deliveries included throughout the study period, 228,586 were identified as cesarean delivery on maternal request. Rates of cesarean delivery on maternal request among all live births increased throughout the study period, from 1% in 1999 to 1.62% in 2015 (P<.0001). Women who delivered by cesarean delivery on maternal request were more likely to be >35 years of age, were in the highest income quartile, and have private insurance. Cesarean delivery on maternal request was associated with an increased risk of venous thromboembolism (odds ratio, 1.9; 95% confidence interval, 1.8-2.0), myocardial infarction (odds ratio, 6.3; 95% confidence interval, 3.8-10.4), sepsis (odds ratio, 5.6; 95% confidence interval, 4.7-6.6), disseminated intravascular coagulation (odds ratio, 2.9; 95% confidence interval, 2.3-3.7), death (odds ratio, 14.5; 95% confidence interval, 11.4-18.6), and prolonged hospital stay (odds ratio, 4.9; 95% confidence interval, 4.8-5.1) and a lower risk of postpartum hemorrhage (odds ratio, 0.7; 95% confidence interval, 0.7-0.7).

CONCLUSION

Our findings indicated that cesarean delivery on maternal request accounts for a small but increasing proportion of all cesarean deliveries in the United States. Cesarean delivery on maternal request was more prevalent among women with certain demographic characteristics, indicating that the option of cesarean delivery on maternal request may be more appealing or more frequently offered to a certain population of women. Although the overall risk of adverse events is low for individual births, population effects can result in increased morbidity and mortality. Therefore, the rates of cesarean delivery on maternal request should be monitored on a national level. Study findings were limited by the absence of a specific diagnostic code for cesarean delivery on maternal request.

Validity of Methods to Identify Individuals With Lower Extremity Amputation Using Department of Veterans Affairs Electronic Medical Records

Objectives

To determine the positive predictive value (PPV) of algorithms to identify patients with major (at the ankle or more proximal) lower extremity amputation (LEA) using Department of Veterans Affairs electronic medical records (EMR) and to evaluate whether PPV varies by sex, age, and race.

Design

We conducted a validation study comparing EMR determined LEA status to self-reported LEA (criterion standard).

Setting

Veterans who receive care at the Department of Veterans Affairs.

Participants

We invited a national sample of patients (N=699) with at least 1 procedure or diagnosis code for major LEA to participate. We oversampled women, Black men, and men ≤40 years of age.

Interventions

Not applicable.

Main Outcome Measure

We calculated PPV estimates and false negative percentages for 7 algorithms using EMR LEA procedure and diagnosis codes relative to self-reported major LEA.

Results

A total of 466 veterans self-reported their LEA status (68%). PPVs for the 7 algorithms ranged from 89% to 100%. The algorithm that required a single diagnosis or procedure code had the lowest PPV (89%). The algorithm that required at least 1 procedure code had the highest PPV (100%) but also had the highest proportion of false negatives (66%). Algorithms that required at least 1 procedure code or 2 or more diagnosis codes 1 month to 1 year apart had high PPVs (98%-99%) but varied in terms of false negative percentages. PPV estimates were higher among men than women but did not differ meaningfully by age or race, after accounting for sex.

Conclusion

PPVs were higher if 1 procedure or at least 2 diagnosis codes were required; the difference between algorithms was marked by sex. Investigators should consider trade-offs between PPV and false negatives to identify patients with LEA using EMRs.

Obstetric comorbidity scores and disparities in severe maternal morbidity across marginalized groups

BACKGROUND

A recently developed obstetrical comorbidity scoring system enables the comparison of severe maternal morbidity rates independent of health status at the time of birth hospitalization. However, the scoring system has not been evaluated in racial-ethnic and socioeconomic groups or used to assess disparities in severe maternal morbidity.

OBJECTIVE

This study aimed to evaluate the performance of an obstetrical comorbidity scoring system when applied across racial-ethnic and socioeconomic groups and to determine the effect of comorbidity score risk adjustment on disparities in severe maternal morbidity.

STUDY DESIGN

We analyzed a population-based cohort of live births that occurred in California during 2011 through 2017 with linked birth certificates and birth hospitalization discharge data (n=3,308,554). We updated a previously developed comorbidity scoring system to include the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modifications diagnosis codes and applied the scoring system to subpopulations (groups) defined by race-ethnicity, nativity, payment method, and educational attainment. We then calculated the risk-adjusted rates of severe maternal morbidity (including and excluding blood transfusion-only cases) for each group and estimated the disparities for these outcomes before and after adjustment for the comorbidity score using logistic regression.

RESULTS

The obstetric comorbidity scores performed consistently across groups (C-statistics ranged from 0.68 to 0.76; calibration curves demonstrated overall excellent prediction of absolute risk). All non-White groups had significantly elevated rates of severe maternal morbidity before and after risk adjustment for comorbidities when compared with the White group (1.3% before, 1.3% after) (American Indian-Alaska Native: 2.1% before, 1.8% after; Asian: 1.5% before, 1.7% after; Black: 2.5% before, 2.0% after; Latinx: 1.6% before, 1.7% after; Pacific Islander: 2.2% before, 1.9% after; and multi-race groups: 1.7% before, 1.6% after). Risk adjustment also modestly increased disparities for the foreign-born group and government insurance groups. Higher educational attainment was associated with decreased severe maternal morbidity rates, which was largely unaffected by comorbidity risk adjustment. The pattern of results was the same whether or not transfusion-only cases were included as severe maternal morbidity.

CONCLUSION

These results support the use of an updated comorbidity scoring system to assess disparities in severe maternal morbidity. Disparities in severe maternal morbidity decreased in magnitude for some racial-ethnic and socioeconomic groups and increased in magnitude for other groups after adjustment for the comorbidity score.

The three-part model for coding causes and mechanisms of healthcare-related adverse events

ICD-11 provides a promising new way to capture healthcare-related harm or injury. In this paper, we elaborate on the framework for describing healthcare-related events where there is a presumed causal link between an event and underlying healthcare-related factors. The three-part model for describing healthcare-related harm or injury in ICD-11 consists of (1) a healthcare-related activity that is the cause of injury or other harm (selected from Chapter 23 of ICD-11); (2) a mode or mechanism of injury or harm, related to the underlying cause (also from Chapter 23 of ICD-11); and (3) the harmful consequences of the event to the patient, selected from any of Chapters 1 through 22 of ICD-11 (most importantly, the injury or harm experienced by the patient). Concepts from these three elements are linked/clustered through postcoordination to reflect the three-part model in a single coded expression. ICD-11 contains many novel features, and the three-part model described here for healthcare-related adverse events is a notable example.

Human Trafficking ICD-10 Code Utilization in Pediatric Tertiary Care Centers Within the United States

BACKGROUND

Human trafficking is a global public health issue that affects pediatric patients widely. The International Labor Organization estimates children comprise approximately 25% of the identified trafficked persons globally, with domestic estimates including over 2000 children a year. Trafficked children experience a broad range of health consequences leading to interface with healthcare systems during their exploitation. In June 2018, International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) released diagnostic codes for human trafficking.

OBJECTIVE

To use a large, multicenter database of US pediatric hospitalizations to describe the utilization of the ICD-10-CM codes related to child trafficking, as well as the demographic and clinical characteristics of these children.

METHODS

This study was descriptive in nature. Encounters using data from the Pediatric Health Information System database (PHIS) with ICD-10-CM codes indicating trafficking from June 1, 2018 to March 1st, 2020 were included in the study cohort, with data collection continuing for 30 days after first hospital encounter, until March 31st, 2020. Patients 19 years old and younger were included. Condition-specific prevalence as well as demographic and clinical characteristics for patient encounters were analyzed. Study subjects were followed for 30 days after first hospital encounter to describe healthcare utilization patterns.

RESULTS

During the study period, 0.005% (n = 293) of patient encounters in the PHIS database were identified as trafficked children. The children of our cohort were mostly female (90%), non-Hispanic Black (38%), and had public insurance (59%). Nearly two-thirds of patients (n = 190) had a documented mental health disorder at the initial encounter, with 32.1% classified as the principal diagnosis. Our cohort had a 30-day hospital inpatient, overnight observation, or emergency department readmission rate of 16% (n = 48).

DISCUSSION

Our study demonstrates a low utilization of human trafficking ICD-10-CM codes in academic children's health centers, with code usage predominantly assigned to Non-Hispanic Black teenage girls. As comparison, in 2019 the National Human Trafficking Hotline identified 2,582 trafficked US children in a single year. These results suggest widespread under-recognition of child trafficking in health care settings, including the intensive care unit, in addition to racial and socioeconomic disparities amongst trafficked children.

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.