Dataset Information

Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.

ABSTRACT:

Background

This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database.

Methods

Random samples of possible cases of each disease (January 2015-January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identified true cases among the random samples of possible cases by medical record review; a third physician made the final decision in cases where the two physicians disagreed. The accuracy of case-finding algorithms was assessed using positive predictive value (PPV) and sensitivity.

Results

There were 2,940 possible cases of malignant tumor; 180 were randomly selected and 108 were identified as true cases after medical record review. One case-finding algorithm gave a high PPV (64.1%) without substantial loss in sensitivity (90.7%) and included ICD-10 codes for malignancy and photographing/imaging. There were 3,559 possible cases of serious infection; 200 were randomly selected and 167 were identified as true cases after medical record review. Two case-finding algorithms gave a high PPV (85.6%) with no loss in sensitivity (100%). Both case-finding algorithms included the relevant diagnostic code and immunological infection test/other related test and, of these, one also included pathological diagnosis within 1 month of hospitalization.

Conclusions

The case-finding algorithms in this study showed good PPV and sensitivity for identification of cases of malignant tumors and serious infections from an administrative healthcare database in Japan.

SUBMITTER: Nishikawa A

PROVIDER: S-EPMC10760479 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.

Nishikawa Atsushi A Yoshinaga Eiko E Nakamura Masaki M Suzuki Masayoshi M Kido Keiji K Tsujimoto Naoto N Ishii Taeko T Koide Daisuke D

Annals of clinical epidemiology 20220107 1

<h4>Background</h4>This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database.<h4>Methods</h4>Random samples of possible cases of each disease (January 2015-January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identi ...[more]

PMID: 38505283

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Project description:BackgroundAdministrative data algorithms (ADAs) to identify pneumonia cases are commonly used in the analysis of pneumonia burden, trends, etiology, processes of care, outcomes, health care utilization, cost, and response to preventative and therapeutic interventions. However, without a good understanding of the validity of ADAs for pneumonia case identification, an adequate appreciation of this literature is difficult. We systematically reviewed the quality and accuracy of published ADAs to identify adult hospitalized pneumonia cases.MethodsWe reviewed the Medline, EMBase, and Cochrane Central databases through May 2020. All studies describing ADAs for adult hospitalized pneumonia and at least one accuracy statistic were included. Investigators independently extracted information about the sampling frame, reference standard, ADA composition, and ADA accuracy.ResultsThirteen studies involving 24 ADAs were analyzed. Compliance with a 38-item study-quality assessment tool ranged from 17 to 29 (median, 23; interquartile range [IQR], 20 to 25). Study setting, design, and ADA composition varied extensively. Inclusion criteria of most studies selected for high-risk populations and/or increased pneumonia likelihood. Reference standards with explicit criteria (clinical, laboratorial, and/or radiographic) were used in only 4 ADAs. Only 2 ADAs were validated (one internally and one externally). ADA positive predictive values ranged from 35.0 to 96.5% (median, 84.8%; IQR, 65.3 to 89.1%). However, these values are exaggerated for an unselected patient population because pneumonia prevalences in the study cohorts were very high (median, 66%; IQR, 46 to 86%). ADA sensitivities ranged from 31.3 to 97.8% (median, 65.1%; IQR 52.5-72.4).DiscussionADAs for identification of adult pneumonia hospitalizations are highly heterogeneous, poorly validated, and at risk for misclassification bias. Greater standardization in reporting ADA accuracy is required in studies using pneumonia ADA for case identification so that results can be properly interpreted.

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Dataset Information

Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.

Background

Methods

Results

Conclusions

Publications

Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database.

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