Development and Validation of a Pediatric Comorbidity Index

Abstract Comorbidity scores are widely used to help address confounding bias in nonrandomized studies conducted within healthcare databases, but existing scores were developed to predict all-cause mortality in adults and may not be appropriate for use in pediatric studies. We developed and validated a pediatric comorbidity index, using healthcare utilization data from the tenth revision of the International Classification of Diseases. Within the MarketScan database, pediatric patients (<18 years) continuously enrolled between October 1, 2015-September 30, 2017 were identified. Logistic regression was used to predict the 1-year risk of hospitalization based on 27 predefined conditions and empirically-identified conditions derived from the most prevalent diagnoses among patients with the outcome. A single numerical index was created by assigning weights to each condition based on its beta coefficient. We conducted internal validation of the index and compared its performance to existing adult scores. The pediatric comorbidity index consisted of 24 conditions and achieved a c-statistic of 0.718 (95% confidence interval [CI] 0.714, 0.723). The index oasutperformed existing adult scores in a pediatric population (c-statistics ranging from 0.522 to 0.640). The pediatric comorbidity index provides a summary measure of disease burden and can be used for risk adjustment in epidemiologic studies of pediatric patients.

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The Charlson Comorbidity Index in Registry-based Research

Methods of Information in Medicine ◽

10.3414/me17-01-0051 ◽

2017 ◽

Vol 56 (05) ◽

pp. 401-406 ◽

Cited By ~ 41

Author(s):

Jesper Lagergren ◽

Nele Brusselaers

Keyword(s):

Royal College ◽

Charlson Comorbidity Index ◽

International Classification Of Diseases ◽

General Purpose ◽

Morbidity Score ◽

Co Morbidity ◽

Classification Of Diseases ◽

Comorbidity Scores ◽

Comorbidity Index ◽

Icd Coding

SummaryBackground: Comorbidities may have an important impact on survival, and comorbidity scores are often implemented in studies assessing prognosis. The Charlson Comorbidity index is most widely used, yet several adaptations have been published, all using slightly different conversions of the International Classification of Diseases (ICD) coding.Objective: To evaluate which coding should be used to assess and quantify comorbidity for the Charlson Comorbidity Index for registry-based research, in particular if older ICD versions will be used.Methods: A systematic literature search was used to identify adaptations and modifications of the ICD-coding of the Charlson Comorbidity Index for general purpose in adults, published in English. Back-translation to ICD version 8 and version 9 was conducted by means of the ICD-code converter of Statistics Sweden.Results: In total, 16 studies were identified reporting ICD-adaptations of the Charlson Comorbidity Index. The Royal College of Surgeons in the United Kingdom combined 5 versions into an adapted and updated version which appeared appropriate for research purposes. Their ICD-10 codes were back-translated into ICD-9 and ICD-8 according to their proposed adaptations, and verified with previous versions of the Charlson Comorbidity Index.Conclusion: Many versions of the Charlson Comorbidity Index are used in parallel, so clear reporting of the version, exact ICD- coding and weighting is necessary to obtain transparency and reproducibility in research. Yet, the version of the Royal College of Surgeons is up-to-date and easy-to-use, and therefore an acceptable co-morbidity score to be used in registry-based research especially for surgical patients.

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Patient Outcomes following Subarachnoid Hemorrhage between the Medical Center and Regional Hospital: Whether All Patients Should Be Transferred to Medical Centers

BioMed Research International ◽

10.1155/2014/927803 ◽

2014 ◽

Vol 2014 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Tsung-Ying Lin ◽

Chieh Hsin Wu ◽

Wei-Che Lee ◽

Chao-Wen Chen ◽

Liang-Chi Kuo ◽

...

Keyword(s):

Health Insurance ◽

Subarachnoid Hemorrhage ◽

Hospital Volume ◽

Medical Center ◽

International Classification Of Diseases ◽

Research Database ◽

Medical Centers ◽

Classification Of Diseases ◽

Comorbidity Index

Subarachnoid hemorrhage (SAH) is a critical illness that may result in patient mortality or morbidity. In this study, we investigated the outcomes of patients treated in medical center and nonmedical center hospitals and the relationship between such outcomes and hospital and surgeon volume. Patient data were abstracted from the National Health Insurance Research Database of Taiwan in the Longitudinal Health Insurance Database 2000, which contains all claims data of 1 million beneficiaries randomly selected in 2000. The International Classification of Diseases, Ninth Revision, subarachnoid hemorrhage (430) was used for the inclusion criteria. We identified 355 patients between 11 and 87 years of age who had subarachnoid hemorrhage. Among them, 32.4% (115/355) were men. The median Charlson comorbidity index (CCI) score was 1.3 (SD ± 0.6). Unadjusted logistic regression analysis demonstrated that low mortality was associated with high hospital volume (OR = 3.21; 95% CI: 1.18–8.77). In this study, we found no statistical significances of mortality, LOS, and total charges between medical centers and nonmedical center hospitals. Patient mortality was associated with hospital volume. Nonmedical center hospitals could achieve resource use and outcomes similar to those of medical centers with sufficient volume.

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Fingertip Injuries in Children: Epidemiology, Financial Burden, and Implications for Prevention

Hand ◽

10.1177/1558944716670139 ◽

2016 ◽

Vol 12 (4) ◽

pp. 342-347 ◽

Cited By ~ 9

Author(s):

Rachel R. Yorlets ◽

Kathleen Busa ◽

Kyle R. Eberlin ◽

Mohammad Ali Raisolsadat ◽

Donald S. Bae ◽

...

Keyword(s):

Average Length ◽

Medical Center ◽

Pediatric Population ◽

Financial Burden ◽

International Classification Of Diseases ◽

Hospital Charges ◽

Average Length Of Stay ◽

Significant Burden ◽

Classification Of Diseases

Background: Although fingertip injuries are common, there is limited literature on the epidemiology and hospital charges for fingertip injuries in children. This descriptive study reports the clinical features of and hospital charges for fingertip injuries in a large pediatric population treated at a tertiary medical center. Methods: Our hospital database was queried using International Classification of Diseases, Revision 9 (ICD-9) codes, and medical records were reviewed. Frequency statistics were generated for 1807 patients with fingertip injuries who presented to the emergency department (ED) at Boston Children’s Hospital (BCH) between 2005 and 2011. Billing records were analyzed for financial data. Results: A total of 1807 patients were identified for this study; 59% were male, and the mean age at time of injury was 8 years. Most commonly, injuries occurred when a finger was crushed (n = 831, 46%) in a door or window. Average length of stay in the ED was 3 hours 45 minutes, 25% of cases needed surgery, and, on average, patients had more than 1 follow-up appointment. About one-third of patients were referred from outside institutions. The average ED charge for fingertip injuries was $1195 in 2014, which would amount to about $320 430 each year (in 2014 dollars) for fingertip injuries presenting to BCH. Conclusion: Fingertip injuries in children are common and result in significant burden, yet are mostly preventable. Most injuries occur at home in a door or window. Although these patients generally heal well, fingertip injuries pose a health, time, and financial burden. Increased awareness and education may help to avoid these injuries.

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Epidemiology and treatment patterns of idiopathic multicentric Castleman disease in the era of IL-6 directed therapy

Blood Advances ◽

10.1182/bloodadvances.2021004441 ◽

2021 ◽

Author(s):

Sudipto Mukherjee ◽

Rabecka Martin ◽

Brenda Sande ◽

Jeremy Paige ◽

David C Fajgenbaum

Keyword(s):

Diagnostic Criteria ◽

International Classification Of Diseases ◽

Epidemiologic Studies ◽

Castleman Disease ◽

Treatment Recommendation ◽

Administrative Claims ◽

Multicentric Castleman Disease ◽

The Us ◽

Classification Of Diseases ◽

Unmet Treatment Need

The epidemiology of HHV-8-negative/idiopathic multicentric Castleman disease (iMCD) remains incompletely understood. Prior epidemiologic studies of CD and iMCD have been hampered by difficulties in accurate case ascertainment due to lack of uniform diagnostic criteria and a disease-specific International Classification of Diseases (ICD) code. In this study, we provide reliable estimates of CD and iMCD in the US using a novel claims-based algorithm that includes CD specific ICD-10 diagnosis code (D47.Z2) supported by presence of ≥2 claims codes corresponding to the minor criteria from the international evidence-based diagnostic criteria for iMCD. We additionally analyzed the treatment classes and patterns in the clinical course of iMCD patients. Using an administrative claims database of 30.7 million individuals enrolled between January 1, 2017 and December 31, 2018, we identified 254 iMCD patients with an estimated annual incidence and prevalence of 3.4 (95% CI, 1.4 - 9.2) and 6.9 (95% CI, 3.7 - 13.3) cases per million, respectively. Among iMCD patients, 39% received corticosteroid monotherapy, 33.1% received no iMCD-directed treatment, and 9.8% received IL-6 targeted therapy with tocilizumab or siltuximab. Siltuximab, which is the only FDA-approved treatment and established first-line treatment recommendation, was used in only 8.7% of iMCD patients. This study provides the most up to date understanding of the iMCD disease burden in the US and identifies a major unmet treatment need for IL-6 directed therapy in this vulnerable cohort.

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Effect of Sex on Clinical Outcomes in Patients with Coronavirus Disease: A Population-Based Study

Journal of Clinical Medicine ◽

10.3390/jcm10010038 ◽

2020 ◽

Vol 10 (1) ◽

pp. 38

Author(s):

Kyu Hyang Cho ◽

Sang Won Kim ◽

Jong Won Park ◽

Jun Young Do ◽

Seok Hui Kang

Keyword(s):

Clinical Outcomes ◽

Kidney Injury ◽

International Classification Of Diseases ◽

Population Based ◽

Charlson Comorbidity Index Score ◽

Population Based Study ◽

Classification Of Diseases ◽

Insurance Claims Data ◽

Comorbidity Index ◽

The Impact

Background: This study aimed to evaluate the association between sex and clinical outcomes in patients with coronavirus disease (COVID-19) using a population-based dataset. Methods: In this retrospective study, insurance claims data from the Korea database were used. Patients who tested positive for COVID-19 were included in the study. All diseases were defined according to the International Classification of Diseases 10th revision. During follow-up, the clinical outcomes, except mortality, were assessed using the electrical codes from the dataset. The clinical outcomes noted were: hospitalization, the use of inotropics, high flow nasal cannula, conventional oxygen therapy, mechanical ventilation, extracorporeal membrane oxygenation, development of acute kidney injury, cardiac arrest, myocardial infarction, acute heart failure, pulmonary embolism, and disseminated intravascular coagulation after the diagnosis of COVID-19. Results: A total of 7327 patients were included; of these, 2964 patients (40.5%) were men and 4363 patients (59.5%) were women. There were no significant differences in the Charlson comorbidity index score between men and women in the same age group. The incidence of mortality and clinical outcomes was higher among men than among women. The mortality rate was the highest for the populations aged 50–64 or ≥65 years. The subgroup analyses for age, diabetes mellitus, or hypertension showed favorable results for patient survival or clinical outcomes for women compared to men. Conclusion: Our population-based study showed that female patients with COVID-19 were associated with favorable outcomes. Furthermore, the impact of sex was more evident in patients aged 50–64 or ≥65 years.

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Performance of Charlson and Elixhauser Comorbidity Index to Predict in-Hospital Mortality in Patients with Stroke in Sumadija and Western Serbia

Iranian Journal of Public Health ◽

10.18502/ijph.v50i5.6114 ◽

2021 ◽

Author(s):

Biljana Bajic ◽

Igor Galic ◽

Natasa Mihailovic ◽

Svetlana Ristic ◽

Svetlana Radevic ◽

...

Keyword(s):

Hospital Mortality ◽

Area Under The Curve ◽

Scoring Systems ◽

International Classification Of Diseases ◽

Administrative Databases ◽

Stroke Patients ◽

Discriminative Performance ◽

Increased Risk ◽

Classification Of Diseases ◽

Comorbidity Index

Background: Comorbidities are major predictors of in-hospital mortality in stroke patients. The Charlson comorbidity index (CCI) and the Elikhauser comorbidity index (ECI) are scoring systems for classifying comorbidities. We aimed to compare the performance of the CCI and ECI to predict in-hospital mortality in stroke patients. Methods: We included patients hospitalized for stroke in the Clinical Center of Kragujevac, Serbia for the last 7 years. Hospitalizations caused by stroke, were identified by the International Classification of Diseases-10 (ICD-10) codes I60.0 - I69.9. All patients were divided into two cohorts: Alive cohort (n=3297) and Mortality cohort (n=978). Results: There were significant associations between higher CCIS and increased risk of in-hospital mortality (HR = 1.07, 95% CI = 1.01–1.12) and between higher ECIS and increased risk of in-hospital mortality (HR = 1.04, 95% CI = 0.99–1.09). Almost 2/3 patients (66.9%) had comorbidities included in the CCI score and 1/3 patients (30.2%) had comorbidities included in the ECI score. The statistically significant higher CCI score (t = -3.88, df = 1017.96, P <0.01) and ECI score (t = -6.7, df = 1447.32, P <0.01) was in the mortality cohort. Area Under the Curve for ECI score was 0.606 and for CCI score was 0.549. Conclusion: Both, the CCI and the ECI can be used as scoring systems for classifying comorbidities in the administrative databases, but the model’s ECI Score had a better discriminative performance of in-hospital mortality in the stroke patients than the CCI Score model.

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Abstract 11: Persistent Risk Of Venous Thromboembolism After Discharge Among Stroke Survivors - A Seven-Years Longitudinal Study

Stroke ◽

10.1161/str.46.suppl_1.11 ◽

2015 ◽

Vol 46 (suppl_1) ◽

Author(s):

Yogesh Moradiya ◽

Devanshi Dharaiya ◽

Santosh Murthy ◽

Christa San Luis ◽

Sneha Modi

Keyword(s):

Risk Factors ◽

Venous Thromboembolism ◽

Metastatic Cancer ◽

International Classification Of Diseases ◽

Incidence Rates ◽

Stroke Survivors ◽

Post Discharge ◽

Classification Of Diseases ◽

Comorbidity Index ◽

Discharge From Hospital

Background and Purpose: Risk of venous thromboembolism (VTE) in stroke patients persists after discharge from hospital due to residual deficits. Quantification of VTE risk at various post-discharge intervals may have implications for surveillance and/or prophylactic strategies for high-risk individuals. We therefore, studied incidence rates and risk factors of deep venous thrombosis (DVT) and pulmonary embolism (PE) among stroke survivors after discharge from the hospital. Methods: Adults with principle diagnosis of ischemic stroke discharged alive from acute care hospitals in California State between January 2005 and December 2010 were followed up until December 2011 using state inpatient and emergency department databases for development of DVT and/or PE. Case ascertainment was done using International Classification of Diseases -9 codes. We calculated incidence rates of DVT and PE during first 30 days, 31-90 days, 91-365 days and >1-year after the discharge. We calculated independent risk factors for VTE within 90 days after discharge by using logistic regression controlling for baseline demographics, comorbidities, inpatient complications and procedures. Results: A total of 168,194 stroke survivors were followed up for a mean duration of 3.6+/-1.9 (SD) years after discharge. Among these, 6,179 (3.7%) developed DVT and 2,487 (1.5%) developed PE resulting in incidence rate of 1.02 and 0.41 per 100 person year for DVT and PE respectively. The incidence rates of DVT during first 30 days, 31-90 days, 91-365 days and >1-year post-discharge were 6.53, 2.91, 1.24 and 0.66 per 100 person year respectively. Similarly, the incidence of PE was 3.04, 1.13, 0.46 and 0.26 during first 30 days, 31-90 days, 91-365 days and >1-year after discharge respectively. Factors independently associated with VTE within 90 days were age>65 years, African-American race, higher Charlson comorbidity index, metastatic cancer, anemia, obesity, acute myocardial infarction, pneumonia, sepsis, hemodialysis, gastrostomy and mechanical ventilation during index admission. Conclusions: VTE risk remains high among stroke survivors even after discharge from the hospital. The risk is highest during first 30 days and remains high as long as 1 year after discharge.

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Identification of Patients with CKD in Medical Databases

Clinical Journal of the American Society of Nephrology ◽

10.2215/cjn.15691020 ◽

2021 ◽

Vol 16 (4) ◽

pp. 543-551

Author(s):

Søren Viborg Vestergaard ◽

Christian Fynbo Christiansen ◽

Reimar Wernich Thomsen ◽

Henrik Birn ◽

Uffe Heide-Jørgensen

Keyword(s):

Kidney Disease ◽

Patient Characteristics ◽

International Classification Of Diseases ◽

Population Based ◽

Epidemiologic Studies ◽

Single Test ◽

Medical Databases ◽

Urine Albumin ◽

Kaplan Meier ◽

Classification Of Diseases

Background and objectivesDespite CKD consensus definitions, epidemiologic studies use multiple different algorithms to identify CKD. We aimed to elucidate if this affects the patient characteristics and the estimated prevalence and prognosis of CKD by applying six different algorithms to identify CKD in population-based medical databases and compare the cohorts.Design, setting, participants, & measurementsPatients with CKD in Northern Denmark (2009–2016) were identified using six different algorithms: five were laboratory based defined by (1) one measured outpatient eGFR <60 ml/min per 1.73 m2 (single test, n=103,435), (2) two such findings ≥90 days apart (Kidney Disease Improving Global Outcomes, n=84,688), (3) two such findings ≥90 days apart with no eGFR >60 ml/min per 1.73 m2 observed in-between (Kidney Disease Improving Global Outcomes, persistent, n=68,994), (4) two such findings ≥90 and <365 days apart (Kidney Disease Improving Global Outcomes, time limited, n=75,031), and (5) two eGFRs <60 ml/min per 1.73 m2 or two urine albumin-creatinine ratios >30 mg/g ≥90 days apart (Kidney Disease Improving Global Outcomes, eGFR/albuminuria, n=100,957). The sixth included patients identified by reported in- and outpatient hospital International Classification of Diseases diagnoses of CKD (hospital-diagnosed, n=27,947). For each cohort, we estimated baseline eGFR, CKD prevalence, and 1-year mortality using the Kaplan–Meier method.ResultsThe five different laboratory-based algorithms resulted in large differences in the estimated prevalence of CKD from 4637–8327 per 100,000 population. In contrast, 1-year mortality varied only slightly (7%–9%). Baseline eGFR levels at diagnosis were comparable (53–56 ml/min per 1.73 m2), whereas median time since first recorded eGFR <60 ml/min per 1.73 m2 varied from 0 months (single-test) to 17 months (Kidney Disease Improving Global Outcomes, persistent). The hospital-diagnosed algorithm yielded markedly lower CKD prevalence (775 per 100,000 population), a lower baseline eGFR (47 ml/min per 1.73 m2), longer time since first eGFR <60 ml/min per 1.73 m2 (median 70 months), and much higher 1-year mortality (22%).ConclusionsPopulation prevalence of CKD identified in medical databases greatly depends on the applied algorithm to define CKD. Despite these differences, laboratory-based algorithms produce cohorts with similar prognosis.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_03_11_CJN15691020_final.mp3

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Validation of the Use of Electronic Health Records for Classification of ADHD Status

Journal of Attention Disorders ◽

10.1177/1087054716672337 ◽

2016 ◽

Vol 23 (13) ◽

pp. 1647-1655 ◽

Cited By ~ 3

Author(s):

Siobhan M. Gruschow ◽

Benjamin E. Yerys ◽

Thomas J. Power ◽

Dennis R. Durbin ◽

Allison E. Curry

Keyword(s):

Large Scale ◽

Pediatric Patients ◽

High Sensitivity ◽

International Classification Of Diseases ◽

Diagnostic Codes ◽

Applied Study ◽

Classification Of Diseases ◽

Primary Care Patients ◽

Electronic Health

Objective: To validate an electronic health record (EHR)–based algorithm to classify ADHD status of pediatric patients. Method: As part of an applied study, we identified all primary care patients of The Children’s Hospital of Philadelphia [CHOP] health care network who were born 1987-1995 and residents of New Jersey. Patients were classified with ADHD if their EHR indicated an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis code of “314.x” at a clinical visit or on a list of known conditions. We manually reviewed EHRs for ADHD patients ( n = 2,030) and a random weighted sample of non-ADHD patients ( n = 807 of 13,579) to confirm the presence or absence of ADHD. Results: Depending on assumptions for inconclusive cases, sensitivity ranged from 0.96 to 0.97 (95% confidence interval [CI] = [0.95, 0.97]), specificity from 0.98 to 0.99 [0.97, 0.99], and positive predictive value from 0.83 to 0.98 [0.81, 0.99]. Conclusion: EHR-based diagnostic codes can accurately classify ADHD status among pediatric patients and can be used by large-scale epidemiologic and clinical studies with high sensitivity and specificity.

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Incidence Estimation in Post-ICU Populations: Challenges and Possible Solutions When Using Claims Data

Das Gesundheitswesen ◽

10.1055/a-1082-0777 ◽

2020 ◽

Vol 82 (S 02) ◽

pp. S101-S107

Author(s):

Magdalena Brandl ◽

Christian Apfelbacher ◽

Annette Weiß ◽

Susanne Brandstetter ◽

Sebastian Edgar Baumeister

Keyword(s):

Intensive Care ◽

Competing Risks ◽

Claims Data ◽

International Classification Of Diseases ◽

Interval Censoring ◽

Competing Risk ◽

Internal Validation ◽

Exact Date ◽

Classification Of Diseases ◽

Icu Treatment

Abstract Background New or worsening cognitive, physical and/or mental health impairments after acute care for critical illness are referred to as “post-intensive care syndrome” (PICS). Little is known about the incidence of its components, since it is challenging to recruit patients after intensive care unit (ICU) treatment for observational studies. Claims data are particularly suited to achieve incidence estimates in difficult-to-recruit groups. However, some limitations remain when using claims data for empirical research on the outcome of ICU treatment. The objective of this article is to describe three challenges and possible solutions for the estimation of the incidence of PICS based on claims data Methodological challenges: The presence of competing risk by death, investigating a syndrome and dealing with interval censoring First, in (post) ICU populations the assumption of independence between the event of interest (diagnosis of PICS component) and the competing event (death) is violated. Competing risk is an event whose occurrence precludes the event of interest to be observed, and in ICU populations, death is a frequent secondary event. Methods that estimate incidence in the presence of competing risks are well-established but have not been applied to the scenario described above. Second, PICS is a complex syndrome and represented by various ICD-10 (International Classification of Diseases, 10th Revision) disease codes. The operationalization of this syndrome (case identification) and the validation of cases are particularly challenging. Third, another major challenge is that the exact date of the event of interest is not available in claims data. It is only known that the event occurred within a certain interval. This feature is called interval censoring. Recently, methods have been developed that address informative censoring due to competing risks in the presence of interval censoring. We will discuss how these methods could be used to tackle the problem when estimating PICS components. Alternatively, it could be possible to assign an exact date for each diagnosis by combining the diagnosis with the exact date of prescriptions of the respective medicines and/or medical services. Conclusion Estimating incidence in post-ICU populations entails various methodological issues when using claims data. Investigators need to be aware of the presence of competing risks. The application of internal validation criteria to operationalize the event of interest is crucial to achieve reliable incidence estimates. The problem of interval censoring can be solved either by statistical methods or by combining information from different sources.

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