Accuracy of lung cancer ICD-9-CM codes in Umbria, Napoli 3 Sud and Friuli Venezia Giulia administrative healthcare databases: a diagnostic accuracy study

Investigating socioeconomic disparities in lung cancer diagnosis, treatment and mortality: an Italian cohort study

BACKGROUND

Lung cancer is one of the most lethal cancers worldwide and patient clinical outcomes seem influenced by their socioeconomic position (SEP). Since little has been investigated on this topic in the Italian context, our aim was to investigate the role of SEP in the care pathway of lung cancer patients in terms of diagnosis, treatment and mortality.

METHODS

This observational retrospective cohort study included patients discharged in the Lazio Region with a lung cancer diagnosis between 2014 and 2017. In the main analysis, educational level was used as SEP measure. Multivariate models, adjusted for demographic and clinical variables, were applied to evaluate the association between SEP and study outcomes, stratified for metastatic (M) and non-metastatic (NM) cancer. We defined a diagnosis as 'delayed' when patients received their initial cancer diagnosis after an emergency department admission. Access to advanced lung cancer treatments (high-cost, novel and innovative treatments) and mortality were investigated within the 24-month period post-diagnosis. Moreover, two additional indicators of SEP were examined in the sensitivity analysis: one focusing on area deprivation and the other on income-based exemption.

RESULTS

A total of 13,251 patients were identified (37.3% with metastasis). The majority were males (> 60%) and over half were older than 70 years. The distribution of SEP levels among patients was as follow: 31% low, 29% medium-low, 32% medium-high and 7% high. As SEP increased, the risks of receiving a delayed diagnosis ((high vs low: M: OR = 0.29 (0.23-0.38), NM: OR = 0.20 (0.16-0.25)) and of mortality ((high vs low M: OR = 0.77 (0.68-0.88) and NM: 0.61 (0.54-0.69)) decreased. Access to advanced lung cancer treatments increased in accordance with SEP only in the M cohort (high vs low: M: OR = 1.57 (1.18-2.09)). The primary findings were corroborated by sensitivity analysis.

CONCLUSIONS

Our study highlighted the need of public health preventive and educational programs in Italy, a country where the care pathway of lung cancer patients, especially in terms of diagnosis and mortality, appears to be negatively affected by SEP level.

How to discriminate non-small cell lung cancer (NSCLC) cases from an Italian administrative database? A retrospective, secondary data use study for evaluating a novel algorithm performance

OBJECTIVES

To evaluate an algorithm developed for identifying non-small cell lung cancer (NSCLC) candidates among patients with lung cancer with a diagnosis International Classification of Diseases: ninth revision (ICD-9) 162.x code in administrative databases. Algorithm could then be applied for identifying the NSCLC population in order to assess the appropriateness and quality of care of the NSCLC care pathway.

DESIGN

Algorithm discrimination capacity to select both NSCLC or non-NSCLC was carried out on a sample for which electronic health record (EHR) diagnosis was available. A bivariate frequency distribution and other measures were used to evaluate algorithm's performances. Associations between possible factors potentially affecting algorithm accuracy were investigated.

SETTING

Administrative databases used in a specific geographical area of Emilia-Romagna region, Italy.

PARTICIPANTS

Algorithm was carried out on patients aged >18 years, with a lung cancer diagnosis from January to December 2017 and resident in Emilia-Romagna region who have been hospitalised at IRST or in one of the hospitals placed in the Forlì-Cesena area and for which EHR diagnosis data were available.

OUTCOME MEASURES

Overall accuracy, positive (PPV) and negative (NPV) predictive values, sensitivity and specificity, positive and negative likelihood ratios and diagnostic OR were calculated.

RESULTS

A total of 430 patients were identified as lung cancer cases based on ICD-9 diagnosis. Focusing on the total incident cases (n=314), the algorithm had an overall accuracy of 82.8% with a sensitivity of 88.8%. The analysis confirmed a high level of PPV (90.2%), but lower specificity (53.7%) and NPV (50%). Higher length of stay seemed to be associated with a correct classification. Hospitalisation regimen and a supply of antiblastic therapy seemed to increase the level of PPV.

CONCLUSION

The algorithm demonstrated a strong validity for identifying NSCLC among patients with lung cancer in hospital administrative databases and can be used to investigate the quality of cancer care for this population.

TRIAL REGISTRATION NUMBER

NCT04676321.

Validity of claims-based algorithms for selected cancers in Japan: Results from the VALIDATE-J study

Purpose

Real‐world data from large administrative claims databases in Japan have recently become available, but limited evidence exists to support their validity. VALIDATE‐J validated claims‐based algorithms for selected cancers in Japan.

Methods

VALIDATE‐J was a multicenter, cross‐sectional, retrospective study. Disease‐identifying algorithms were used to identify cancers diagnosed between January or March 2012 and December 2016 using claims data from two hospitals in Japan. Positive predictive values (PPVs), specificity, and sensitivity were calculated for prevalent (regardless of baseline cancer‐free period) and incident (12‐month cancer‐free period; with claims and registry periods in the same month) cases, using hospital cancer registry data as gold standard.

Results

22 108 cancers were identified in the hospital claims databases. PPVs (number of registry cases) for prevalent/incident cases were: any malignancy 79.0% (25 934)/73.1% (18 119); colorectal 84.4% (3519)/65.6% (2340); gastric 87.4% (3534)/76.8% (2279); lung 88.1% (2066)/79.9% (1636); breast 86.4% (4959)/59.9% (3185); pancreatic 87.1% (582)/80.4% (508); melanoma 48.7% (46)/42.9% (36); and lymphoma 83.6% (1457)/77.8% (1035). Specificity ranged from 98.3% to 100% (prevalent)/99.5% to 100% (incident); sensitivity ranged from 39.1% to 67.6% (prevalent)/12.5% to 31.4% (incident). PPVs of claims‐based algorithms for several cancers in patients ≥66 years of age were slightly higher than those in a US Medicare population.

Conclusions

VALIDATE‐J demonstrated high specificity and modest‐to‐moderate sensitivity for claims‐based algorithms of most malignancies using Japanese claims data. Use of claims‐based algorithms will enable identification of patient populations from claims databases, while avoiding direct patient identification. Further research is needed to confirm the generalizability of our results and applicability to specific subgroups of patient populations.

Further evidence that endometriosis is related to tubal and ovarian cancers: A study of 271,444 inpatient women

OBJECTIVE

To evaluate associations between endometriosis and tubal and ovarian cancers in a large population-based study.

METHODS

The Health Care Cost and Utilization Project - National Inpatient Sample databases from 2005 to 2014 were used in this study. Data on patients with a diagnosis of tubal or ovarian cancer and endometriosis (overall and subtypes including adenomyosis and pelvic endometriosis) using International Classification of Diseases, Ninth Edition, Clinical Modification codes were extracted. Logistic regression analysis was performed to evaluate associations between tubal and ovarian cancers and endometriosis. Adjustment was made for age, race, median income level, payment plan, hospital location and obesity.

RESULTS

Of 38,800,139 women aged >18 years who were hospitalized between 2005 and 2014, 271,444 women with adenomyosis and/or pelvic endometriosis, 4289 women with tubal cancer and 133,253 women with ovarian cancer were identified. The rate of tubal cancer was three-fold higher in women with endometriosis compared with women without endometriosis (0.03 % vs 0.01 %). The odds ratio (OR) adjusted for age, race, obesity, income and insurance type was 4.02 [95 % confidence interval (CI) 3.17-5.11; p < 0.01]. The rate of tubal cancer was higher in women with adenomyosis (0.04 % vs 0.01 %; adjusted OR 4.88, 95 % CI 3.66-6.50; p < 0.01) and women with pelvic endometriosis (0.02 % vs 0.01 %; adjusted OR 2.80, 95 % CI 1.84-4.27; p < 0.01) compared with women without these conditions. Similar associations were found between ovarian cancer and pelvic endometriosis and ovarian cancer and adenomyosis.

CONCLUSION

Both pelvic endometriosis and adenomyosis are strongly associated with tubal and ovarian cancers.

Validity of cerebrovascular ICD-9-CM codes in healthcare administrative databases. The Umbria Data-Value Project

Background

Validation of administrative databases for cerebrovascular diseases is crucial for epidemiological, outcome, and health services research. The aim of this study was to validate ICD-9 codes for hemorrhagic or ischemic stroke in administrative databases, to use them for a comprehensive assessment of the burden of disease in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources.

Methods

We considered the hospital discharge abstract database of the Umbria Region (890,000 residents). Source population was represented by patients aged >18 discharged from hospital with a diagnosis of hemorrhagic or ischemic stroke between 2012 and 2014 using ICD-9-CM codes in primary position. We randomly selected and reviewed medical charts of cases and non-cases from hospitals. For case ascertainment we considered symptoms and instrumental tests reported in the medical charts. Diagnostic accuracy measures were computed using 2x2 tables.

Results

We reviewed 767 medical charts for cases and 78 charts for non-cases. Diagnostic accuracy measures were: subarachnoid hemorrhage: sensitivity (SE) 100% (95% CI: 97%-100%), specificity (SP) 96% (90–99), positive predictive value (PPV) 98% (93–100), negative predictive value (NPV) 100% (95–100); intracerebral hemorrhage: SE 100% (97–100), SP 98% (91–100), PPV 98% (94–100), NPV 100% (95–100); other and unspecified intracranial hemorrhage: SE 100% (97–100), SP 96% (90–99), PPV 98% (93–100), NPV 100% (95–100); ischemic stroke due to occlusion and stenosis of precerebral arteries: SE 99% (94–100), SP 66 (57–75), PPV 70% (61–77), NPV 99% (93–100); occlusion of cerebral arteries: SE 100% (97–100), SP 87% (78–93), PPV 91% (84–95), NPV 100% (95–100); acute, but ill-defined, cerebrovascular disease: SE 100% (97–100), SP 78% (69–86), PPV % 83 (75–89), NPV 100% (95–100).

Conclusions

Case ascertainment for both ischemic and hemorrhagic stroke showed good or high levels of accuracy within the regional healthcare databases in Umbria. This database can confidently be employed for epidemiological, outcome, and health services research related to any type of stroke.

A diagnostic accuracy study validating cardiovascular ICD-9-CM codes in healthcare administrative databases. The Umbria Data-Value Project

BACKGROUND

Administrative healthcare databases are useful and inexpensive tools that can provide a comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. However, a crucial issue is the reliability of information gathered. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research.

METHODS

Data from the centralised administrative database of the Umbria Region (890,000 residents, located in Central Italy) were considered. Patients with a first hospital discharge for AMI, AF/flutter, and HF, between 2012 and 2014, were identified using ICD-9-CM codes in primary position. A sample of cases and non-cases was randomly selected, and the corresponding medical charts reviewed by specifically trained investigators. For each disease, case ascertainment was based on all clinical, laboratory, and instrumental examinations available in medical charts. Sensitivity, specificity, and predictive values with 95% confidence intervals (CIs), were calculated.

RESULTS

We reviewed 458 medical charts, 128 for AMI, 127 for AF/flutter, 127 for HF, and 76 of non-cases for each condition. Diagnostic accuracy measures of the original discharge diagnosis were as follows. AMI: sensitivity 98% (95% CI, 94-100%), specificity 91% (95% CI, 83-97%), positive predictive value (PPV) 95% (95% CI, 89-98%), negative predictive value (NPV) 97% (95% CI, 91-100%). AF/flutter: sensitivity 95% (95% CI, 90-98%), specificity 95% (95% CI, 87-99%), PPV 97% (95% CI, 92-99%), NPV 92% (95% CI, 84-97%). HF: sensitivity 96% (95% CI, 91-99%), specificity 90% (95% CI, 81-96%), PPV 94% (95% CI, 88-97%), NPV 93% (95% CI, 85-98%).

CONCLUSION

The case ascertainment for AMI, AF and flutter, and HF, showed a high level of accuracy (≥ 90%). The healthcare administrative database of the Umbria Region can be confidently used for epidemiological, outcome, and health services research.

Sensitivity and specificity of breast cancer ICD-9-CM codes in three Italian administrative healthcare databases: a diagnostic accuracy study

OBJECTIVES

To assess the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes in identifying patients diagnosed with incident carcinoma in situ and invasive breast cancer in three Italian administrative databases.

DESIGN

A diagnostic accuracy study comparing ICD-9-CM codes for carcinoma in situ (233.0) and for invasive breast cancer (174.x) with medical chart (as a reference standard). Case definition: (1) presence of a primary nodular lesion in the breast and (2) cytological or histological documentation of cancer from a primary or metastatic site.

SETTING

Administrative databases from Umbria Region, Azienda Sanitaria Locale (ASL) Napoli 3 Sud (NA) and Friuli VeneziaGiulia (FVG) Region.

PARTICIPANTS

Women with breast carcinoma in situ (n=246) or invasive breast cancer (n=384) diagnosed (in primary position) between 2012 and 2014.

OUTCOME MEASURES

Sensitivity and specificity for codes 233.0 and 174.x.

RESULTS

For invasive breast cancer the sensitivities were 98% (95% CI 93% to 99%) for Umbria, 96% (95% CI 91% to 99%) for NA and 100% (95% CI 97% to 100%) for FVG. Specificities were 90% (95% CI 82% to 95%) for Umbria, 91% (95% CI 83% to 96%) for NA and 91% (95% CI 84% to 96%) for FVG.For carcinoma in situ the sensitivities were 100% (95% CI 93% to 100%) for Umbria, 100% (95% CI 95% to 100%) for NA and 100% (95% CI 96% to 100%) for FVG. Specificities were 98% (95% CI 93% to 100%) for Umbria, 86% (95% CI 78% to 92%) for NA and 90% (95% CI 82% to 95%) for FVG.

CONCLUSIONS

Administrative healthcare databases from Umbria, NA and FVG are accurate in identifying hospitalised news cases of carcinoma of the breast. The proposed case definition is a powerful tool to perform research on large populations of newly diagnosed patients with breast cancer.

Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review

OBJECTIVE

To define the accuracy of administrative datasets to identify primary diagnoses of breast cancer based on the International Classification of Diseases (ICD) 9th or 10th revision codes.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE, EMBASE, Web of Science and the Cochrane Library (April 2017).

ELIGIBILITY CRITERIA

The inclusion criteria were: (a) the presence of a reference standard; (b) the presence of at least one accuracy test measure (eg, sensitivity) and (c) the use of an administrative database.

DATA EXTRACTION

Eligible studies were selected and data extracted independently by two reviewers; quality was assessed using the Standards for Reporting of Diagnostic accuracy criteria.

DATA ANALYSIS

Extracted data were synthesised using a narrative approach.

RESULTS

From 2929 records screened 21 studies were included (data collection period between 1977 and 2011). Eighteen studies evaluated ICD-9 codes (11 of which assessed both invasive breast cancer (code 174.x) and carcinoma in situ (ICD-9 233.0)); three studies evaluated invasive breast cancer-related ICD-10 codes. All studies except one considered incident cases.The initial algorithm results were: sensitivity ≥80% in 11 of 17 studies (range 57%-99%); positive predictive value was ≥83% in 14 of 19 studies (range 15%-98%) and specificity ≥98% in 8 studies. The combination of the breast cancer diagnosis with surgical procedures, chemoradiation or radiation therapy, outpatient data or physician claim may enhance the accuracy of the algorithms in some but not all circumstances. Accuracy for breast cancer based on outpatient or physician's data only or breast cancer diagnosis in secondary position diagnosis resulted low.

CONCLUSION

Based on the retrieved evidence, administrative databases can be employed to identify primary breast cancer. The best algorithm suggested is ICD-9 or ICD-10 codes located in primary position.

TRIAL REGISTRATION NUMBER

CRD42015026881.

Accuracy of colorectal cancer ICD-9-CM codes in Italian administrative healthcare databases: a cross-sectional diagnostic study

Objectives To assess the accuracy of International Classification of Diseases, Ninth Revision - Clinical Modification (ICD-9-CM) codes in identifying subjects with colorectal cancer.

DESIGN

A diagnostic accuracy study comparing ICD-9-CM codes (index test) for colorectal cancers with medical chart (as a reference standard). Case ascertainment based on neoplastic lesion(s) within the colon/rectum and histological documentation from a primary or metastatic site positive for colorectal cancer.

SETTING

Administrative databases from the Umbria region, Azienda Sanitaria Locale (ASL) Napoli 3 Sud (NA) region and Friuli Venezia Giulia (FVG) region.

PARTICIPANTS

We randomly selected 130 incident patients from each hospital discharge database, admitted between 2012 and 2014, having colorectal cancer ICD-9 codes located in primary position, and 94 non-cases, that is, patients having a diagnosis of cancer (ICD-9 140-239) other than colorectal cancer in primary position.

OUTCOME MEASURES

Sensitivity, specificity and predictive values for 153.x code (colon cancer) and for 154.x code (rectal cancer).

RESULTS

The positive predictive value (PPV) for colon cancer diagnoses was 80% for Umbria (95% CI 73% to 87%), 81% for NA (95% CI 73% to 88%) and 80% for FVG (95% CI 72% to 87%).The sensitivity ranged from 98% to 99%, while the specificity ranged from 78% to 80% in the three units.For rectal cancer, the PPV was 84% for Umbria (95% CI 77% to 90%), 80% for NA (95% CI 72% to 87%) and 81% for FVG (95% CI 73% to 87%). The sensitivities ranged from 98% to 100%, while the specificity estimates from 79% to 82%.

CONCLUSIONS

Administrative databases in Italy can be a valuable tool for cancer surveillance as well as monitoring geographical and temporal variation of cancer practice.