Validity of ICD-9-CM codes for breast, lung and colorectal cancers in three Italian administrative healthcare databases: a diagnostic accuracy study protocol

Incidence and Prevalence Analysis of Non-Small-Cell and Small-Cell Lung Cancer Using Administrative Data

Treatment of lung cancer depends on the stage of the tumor and the histological type. In recent years, the histological confirmation of lung non-small-cell lung cancer has become crucial since the availability of selective target therapeutic approaches. The aim of the study was to develop a validated procedure to estimate the incidence and prevalence of non-small-cell and small-cell lung cancer from healthcare administrative data. A latent class model for categorical variables was applied. The following observed variables were included in the analysis: ICD-9-CM codes in the Hospital Discharge Registry, ATC codes of medications dispensed present in the Drugs Prescriptions Registry, and the procedure codes in the Outpatient Registry. The proportion of non-small-cell lung cancer diagnoses was estimated to be 85% of the total number of lung cancer on the cohort of incident cases and 89% on the cohort of prevalent cases. External validation on a cohort of 107 patients with a lung cancer diagnosis and histological confirmation showed a sensitivity of 95.6% (95%CI: 89–98.8%) and specificity of 94.1% (95%CI: 71.3–99.9%). The procedure is an easy-to-use tool to design subpopulation-based studies on lung cancer and to better plan resource allocation, which is important since the introduction of new targeted therapies in non-small-cell lung carcinoma.

FasTag: Automatic text classification of unstructured medical narratives

Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.

Spironolactone use does not increase the risk of female breast cancer recurrence: A retrospective analysis

BACKGROUND

Spironolactone is used off-label for androgenic alopecia because of its ability to arrest hair loss progression and long-term safety profile. However, little is known about the safety of spironolactone in breast cancer (BC) survivors. Because spironolactone has estrogenic effects, there is a theoretical risk for BC recurrence. Given that spironolactone is an important tool in the treatment of alopecia, we investigated whether spironolactone increased risk for BC recurrence.

OBJECTIVE

To determine whether spironolactone is associated with increased BC recurrence.

METHODS

A retrospective analysis was conducted using the Humana Insurance database. Patients with a history of BC were identified using International Classification of Diseases codes, stratified by spironolactone prescription, and also matched 1:1 using propensity score analysis. Patient characteristics and cancer recurrence rates between both cohorts were compared and analyzed.

RESULTS

BC recurrence developed in 123 patients (16.5%) who were prescribed spironolactone compared with 3649 patients (12.8%) who developed BC recurrence without spironolactone prescribed (P = .004). After propensity matching, adjusted Cox regression analysis showed no association between spironolactone and increased BC recurrence (adjusted hazard ratio, 0.966; 95% confidence interval, 0.807-1.156; P = .953).

LIMITATIONS

Retrospective study.

CONCLUSION

Spironolactone was not independently associated with increased BC recurrence and may be considered for the treatment of alopecia in BC survivors.

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.

Calcium Channel Blockers and Risk of Lymphedema among Breast Cancer Patients: Nested Case-Control Study

BACKGROUND

To assess the risk of lymphedema associated with the use of calcium channel blockers (CCB) among breast cancer patients.

METHODS

A nested case-control study of adult female breast cancer patients receiving an antihypertensive agent was conducted using administrative claims data between 2007 and 2015. Cases were patients with lymphedema who were matched to 5 controls based on nest entry date (±180 days), age (±5 years), number of hypertensive drug classes, Charlson Comorbidity Index (CCI), thiazide exposure, and insurance type. Exposure to CCBs and covariates was identified in the 180-day period prior to event date. Conditional logistic regression was used to assess the impact of exposure among cases and controls.

RESULTS

A total of 717 cases and 1,681 matched controls were identified. After matching on baseline characteristics, mastectomy (7.8% vs. 4.8%; P = 0.0039), exposure to radiotherapy (27.1% vs. 21.7%; P = 0.0046), taxane-based chemotherapy (11.7% vs. 7.4%; P = 0.0007), anthracycline-based chemotherapy (6.0% vs. 3.6%; P = 0.0073), CCB use (28.3% vs. 23.3%; P = 0.0087), and CCI (19.8% vs. 12.7%; P < 0.0001; score of 4 or above) were all higher in cases during the 180 days prior to the event date. In the adjusted analysis, CCB exposure was significantly associated with increased risk of lymphedema (OR = 1.320; 95% confidence interval, 1.003-1.737).

CONCLUSIONS

CCB use was significantly associated with the development of lymphedema in breast cancer patients.

IMPACT

CCBs should be avoided or used with caution in breast cancer patients to reduce the risk for developing lymphedema.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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

OBJECTIVES

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

DESIGN

A cross-sectional diagnostic accuracy study comparing ICD-9-CM 162.x code (index test) in primary position with medical chart (reference standard). Case ascertainment was based on the presence of a primary nodular lesion in the lung and cytological or histological documentation of cancer from a primary or metastatic site.

SETTING

Three operative units: administrative databases from Umbria Region (890 000 residents), ASL Napoli 3 Sud (NA) (1 170 000 residents) and Friuli Venezia Giulia (FVG) Region (1 227 000 residents).

PARTICIPANTS

Incident subjects with lung cancer (n=386) diagnosed in primary position between 2012 and 2014 and a population of non-cases (n=280).

OUTCOME MEASURES

Sensitivity, specificity and positive predictive value (PPV) for 162.x code.

RESULTS

130 cases and 94 non-cases were randomly selected from each database and the corresponding medical charts were reviewed. Most of the diagnoses for lung cancer were performed in medical departments.True positive rates were high for all the three units. Sensitivity was 99% (95% CI 95% to 100%) for Umbria, 97% (95% CI 91% to 100%) for NA, and 99% (95% CI 95% to 100%) for FVG. The false positive rates were 24%, 37% and 23% for Umbria, NA and FVG, respectively. PPVs were 79% (73% to 83%)%) for Umbria, 58% (53% to 63%)%) for NA and 79% (73% to 84%)%) for FVG.

CONCLUSIONS

Case ascertainment for lung cancer based on imaging or endoscopy associated with histological examination yielded an excellent sensitivity in all the three administrative databases. PPV was moderate for Umbria and FVG but lower for NA.

Validating malignant melanoma ICD-9-CM codes in Umbria, ASL Napoli 3 Sud and Friuli Venezia Giulia 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 subjects with melanoma.

DESIGN

A diagnostic accuracy study comparing melanoma ICD-9-CM codes (index test) with medical chart (reference standard). Case ascertainment was based on neoplastic lesion of the skin and a histological diagnosis from a primary or metastatic site positive for melanoma.

SETTING

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

PARTICIPANTS

112, 130 and 130 cases (subjects with melanoma) were randomly selected from Umbria, NA and FVG, respectively; 94 non-cases (subjects without melanoma) were randomly selected from each unit.

OUTCOME MEASURES

Sensitivity and specificity for ICD-9-CM code 172.x located in primary position.

RESULTS

The most common melanoma subtype was malignant melanoma of skin of trunk, except scrotum (ICD-9-CM code: 172.5), followed by malignant melanoma of skin of lower limb, including hip (ICD-9-CM code: 172.7). The mean age of the patients ranged from 60 to 61 years. Most of the diagnoses were performed in surgical departments.The sensitivities were 100% (95% CI 96% to 100%) for Umbria, 99% (95% CI 94% to 100%) for NA and 98% (95% CI 93% to 100%) for FVG. The specificities were 88% (95% CI 80% to 93%) for Umbria, 77% (95% CI 69% to 85%) for NA and 79% (95% CI 71% to 86%) for FVG.

CONCLUSIONS

The case definition for melanoma based on clinical or instrumental diagnosis, confirmed by histological examination, showed excellent sensitivities and good specificities in the three operative units. Administrative databases from the three operative units can be used for epidemiological and outcome research of melanoma.

Validation of claims-based algorithms for pulmonary arterial hypertension

Administrative claims studies do not adequately distinguish pulmonary arterial hypertension (PAH) from other forms of pulmonary hypertension (PH). Our aim is to develop and validate a set of algorithms using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and electronic medical records (EMR), to identify patients with PAH. From January 2012 to August 2015, the EMRs of patients with ICD-9-CM codes for PH with an outpatient visit at the University of Texas Medical Branch were reviewed. Patients were divided into PAH or non-PAH groups according to EMR encounter diagnosis. Patient demographics, echocardiography, right heart catheterization (RHC) results, and PAH-specific therapies were assessed. RHC measurements were reviewed to categorize cases as hemodynamically determined PAH or not PAH. Weighted sensitivity, specificity, and positive and negative predictive values were calculated for the developed algorithms. A logistic regression analysis was conducted to determine how well the algorithms performed. External validation was performed at the University of Virginia Health System. The cohort for the development algorithms consisted of 683 patients with PH, PAH group (n = 191) and non-PAH group (n = 492). A hemodynamic diagnosis of PAH determined by RHC was recorded in the PAH (26%) and non-PAH (3%) groups. The positive predictive value for the algorithm that included ICD-9-CM and PAH-specific medications was 66.9% and sensitivity was 28.2% with a c-statistic of 0.66. The positive predictive value for the EMR-based algorithm that included ICD-9-CM, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medication was 69.4% and a c-statistic of 0.87. A validation cohort of 177 patients with PH examined from August 2015 to August 2016 using EMR-based algorithms yielded a similar positive predictive value of 62.5%. In conclusion, claims-based algorithms that included ICD-9-CM codes, EMR encounter diagnosis, echocardiography, RHC, and PAH-specific medications better-identified patients with PAH than ICD-9-CM codes alone.

Clinical and Economic Consequences of Early Cancer After Kidney Transplantation in Contemporary Practice

BACKGROUND

Current clinical and economic consequences of cancer after kidney transplantation are incompletely defined.

METHODS

We examined United States Renal Data System records of Medicare-insured kidney transplant recipients in 2000 to 2011 to determine clinical and economic impacts of cancer diagnosed within the first 3 years posttransplantation. Cancer diagnoses were identified using Medicare billing codes and categorized as nonmelanoma skin cancer (NMSC), viral-linked and "other" cancers. Associations of cancers with mortality and graft loss were estimated by time-varying Cox regression. Impacts of cancer diagnoses on inpatient and outpatient costs within each year were quantified by multivariate linear regression modeling.

RESULTS

Among 67 157 recipients, by 3 years posttransplant, NMSC was diagnosed in 5.7%, viral-linked cancer in 1.9%, and "other" cancers in 6.3%. Viral-linked cancer was associated with more than 3-fold increased risk in subsequent mortality until the third transplant anniversary, and nearly twice the mortality risk after year 3. "Other" cancers had similar associations with death and graft loss, whereas NMSC was associated with 33% higher mortality beyond the third year posttransplant. Viral-linked cancer had the largest inpatient and outpatient cost impacts per case, followed by "other" cancer, whereas NMSC impacted only outpatient costs. Care of new cancer diagnoses was generally more costly than care of previously established diagnoses. Cancer accounted for 3% to 5.5% of total inpatient Medicare expenditures and 1.5% to 3.3% of outpatient expenditures in the first 3 years posttransplant.

CONCLUSIONS

Early posttransplant malignancy is an expensive and morbid condition that warrants attention in efforts to improve pretransplant screening and management protocols before and after transplant.

Protocol for validating cardiovascular and cerebrovascular ICD-9-CM codes in healthcare administrative databases: the Umbria Data Value Project

INTRODUCTION

Administrative healthcare databases 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, thus providing answers to a wide spectrum of research questions. However, a crucial issue is the reliability of information gathered. Aim of this protocol is to validate International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) codes for major cardiovascular diseases, including acute myocardial infarction (AMI), heart failure (HF), atrial fibrillation (AF) and stroke.

METHODS AND ANALYSIS

Data from the centralised administrative database of the entire Umbria Region (910 000 residents, located in Central Italy) will be considered. Patients with a first hospital discharge for AMI, HF, AF or stroke, between 2012 and 2014, will be identified in the administrative database using the following groups of ICD-9-CM codes located in primary position: (1) 410.x for AMI; (2) 427.31 for AF; (3) 428 for HF; (4) 433.x1, 434 (excluding 434.x0), 436 for ischaemic stroke, 430 and 431 for haemorrhagic stroke (subarachnoid haemorrhage and intracerebral haemorrhage). A random sample of cases, and of non-cases, will be selected, and the corresponding medical charts retrieved and reviewed for validation by pairs of trained, independent reviewers. For each condition considered, case adjudication of disease will be based on symptoms, laboratory and diagnostic tests, as available in medical charts. Divergences will be resolved by consensus. Sensitivity and specificity with 95% CIs will be calculated.

ETHICS AND DISSEMINATION

Research protocol has been granted approval by the Regional Ethics Committee. Study results will be disseminated widely through peer-reviewed publications and presentations at national and international conferences.

Validity of peptic ulcer disease and upper gastrointestinal bleeding diagnoses in administrative databases: a systematic review protocol

INTRODUCTION

Administrative healthcare databases are useful to investigate the epidemiology, health outcomes, quality indicators and healthcare utilisation concerning peptic ulcers and gastrointestinal bleeding, but the databases need to be validated in order to be a reliable source for research. The aim of this protocol is to perform the first systematic review of studies reporting the validation of International Classification of Diseases, 9th Revision and 10th version (ICD-9 and ICD-10) codes for peptic ulcer and upper gastrointestinal bleeding diagnoses.

METHODS AND ANALYSIS

MEDLINE, EMBASE, Web of Science and the Cochrane Library databases will be searched, using appropriate search strategies. We will include validation studies that used administrative data to identify peptic ulcer disease and upper gastrointestinal bleeding diagnoses or studies that evaluated the validity of peptic ulcer and upper gastrointestinal bleeding codes in administrative data. The following inclusion criteria will be used: (a) the presence of a reference standard case definition for the diseases of interest; (b) the presence of at least one test measure (eg, sensitivity, etc) and (c) the use of an administrative database as a source of data. Pairs of reviewers will independently abstract data using standardised forms and will evaluate quality using the checklist of the Standards for Reporting of Diagnostic Accuracy (STARD) criteria. This systematic review protocol has been produced in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA-P) 2015 statement.

ETHICS AND DISSEMINATION

Ethics approval is not required given that this is a protocol for a systematic review. We will submit results of this study to a peer-reviewed journal for publication. The results will serve as a guide for researchers validating administrative healthcare databases to determine appropriate case definitions for peptic ulcer disease and upper gastrointestinal bleeding, as well as to perform outcome research using administrative healthcare databases of these conditions.

TRIAL REGISTRATION NUMBER

CRD42015029216.

Validation of chronic obstructive pulmonary disease (COPD) diagnoses in healthcare databases: a systematic review protocol

INTRODUCTION

Healthcare databases are useful sources to investigate the epidemiology of chronic obstructive pulmonary disease (COPD), to assess longitudinal outcomes in patients with COPD, and to develop disease management strategies. However, in order to constitute a reliable source for research, healthcare databases need to be validated. The aim of this protocol is to perform the first systematic review of studies reporting the validation of codes related to COPD diagnoses in healthcare databases.

METHODS AND ANALYSIS

MEDLINE, EMBASE, Web of Science and the Cochrane Library databases will be searched using appropriate search strategies. Studies that evaluated the validity of COPD codes (such as the International Classification of Diseases 9th Revision and 10th Revision system; the Real codes system or the International Classification of Primary Care) in healthcare databases will be included. Inclusion criteria will be: (1) the presence of a reference standard case definition for COPD; (2) the presence of at least one test measure (eg, sensitivity, positive predictive values, etc); and (3) the use of a healthcare database (including administrative claims databases, electronic healthcare databases or COPD registries) as a data source. Pairs of reviewers will independently abstract data using standardised forms and will assess quality using a checklist based on the Standards for Reporting of Diagnostic accuracy (STARD) criteria. This systematic review protocol has been produced in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) 2015 statement.

ETHICS AND DISSEMINATION

Ethics approval is not required. Results of this study will be submitted to a peer-reviewed journal for publication. The results from this systematic review will be used for outcome research on COPD and will serve as a guide to identify appropriate case definitions of COPD, and reference standards, for researchers involved in validating healthcare databases.

TRIAL REGISTRATION NUMBER

CRD42015029204.