Validation of discharge diagnosis codes to identify serious infections among middle age and older adults

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Abstract

Objectives

Hospitalisations for serious infections are common among middle age and older adults and frequently used as study outcomes. Yet, few studies have evaluated the performance of diagnosis codes to identify serious infections in this population. We sought to determine the positive predictive value (PPV) of diagnosis codes for identifying hospitalisations due to serious infections among middle age and older adults.

Setting and participants

We identified hospitalisations for possible infection among adults >=50 years enrolled in the Tennessee Medicaid healthcare programme (2008–2012) using International Classifications of Diseases, Ninth Revision diagnosis codes for pneumonia, meningitis/encephalitis, bacteraemia/sepsis, cellulitis/soft-tissue infections, endocarditis, pyelonephritis and septic arthritis/osteomyelitis.

Design

Medical records were systematically obtained from hospitals randomly selected from a stratified sampling framework based on geographical region and hospital discharge volume.

Measures

Two trained clinical reviewers used a standardised extraction form to abstract information from medical records. Predefined algorithms served as reference to adjudicate confirmed infection-specific hospitalisations. We calculated the PPV of diagnosis codes using confirmed hospitalisations as reference. Sensitivity analyses determined the robustness of the PPV to definitions that required radiological or microbiological confirmation. We also determined inter-rater reliability between reviewers.

Results

The PPV of diagnosis codes for hospitalisations for infection (n=716) was 90.2% (95% CI 87.8% to 92.2%). The PPV was highest for pneumonia (96.5% (95% CI 93.9% to 98.0%)) and cellulitis (91.1% (95% CI 84.7% to 94.9%)), and lowest for meningitis/encephalitis (50.0% (95% CI 23.7% to 76.3%)). The adjudication reliability was excellent (92.7% agreement; first agreement coefficient: 0.91). The overall PPV was lower when requiring microbiological confirmation (45%) and when requiring radiological confirmation for pneumonia (79%).

Conclusions

Discharge diagnosis codes have a high PPV for identifying hospitalisations for common, serious infections among middle age and older adults. PPV estimates for rare infections were imprecise.

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Most cited references 47

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Identifying patients with severe sepsis using administrative claims: patient-level validation of the angus implementation of the international consensus conference definition of severe sepsis.

Preeti Malani, Latoya Kuhn, Andrew Odden … (2014)

Severe sepsis is a common and costly problem. Although consistently defined clinically by consensus conference since 1991, there have been several different implementations of the severe sepsis definition using ICD-9-CM codes for research. We conducted a single center, patient-level validation of 1 common implementation of the severe sepsis definition, the so-called "Angus" implementation. Administrative claims for all hospitalizations for patients initially admitted to general medical services from an academic medical center in 2009-2010 were reviewed. On the basis of ICD-9-CM codes, hospitalizations were sampled for review by 3 internal medicine-trained hospitalists. Chart reviews were conducted with a structured instrument, and the gold standard was the hospitalists' summary clinical judgment on whether the patient had severe sepsis. Three thousand one hundred forty-six (13.5%) hospitalizations met ICD-9-CM criteria for severe sepsis by the Angus implementation (Angus-positive) and 20,142 (86.5%) were Angus-negative. Chart reviews were performed for 92 randomly selected Angus-positive and 19 randomly-selected Angus-negative hospitalizations. Reviewers had a κ of 0.70. The Angus implementation's positive predictive value was 70.7% [95% confidence interval (CI): 51.2%, 90.5%]. The negative predictive value was 91.5% (95% CI: 79.0%, 100%). The sensitivity was 50.4% (95% CI: 14.8%, 85.7%). Specificity was 96.3% (95% CI: 92.4%, 100%). Two alternative ICD-9-CM implementations had high positive predictive values but sensitivities of <20%. The Angus implementation of the international consensus conference definition of severe sepsis offers a reasonable but imperfect approach to identifying patients with severe sepsis when compared with a gold standard of structured review of the medical chart by trained hospitalists.

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The era of molecular and other non-culture-based methods in diagnosis of sepsis.

Nicasio Mancini, Silvia Carletti, Nadia Ghidoli … (2010)

Sepsis, a leading cause of morbidity and mortality throughout the world, is a clinical syndrome with signs and symptoms relating to an infectious event and the consequent important inflammatory response. From a clinical point of view, sepsis is a continuous process ranging from systemic inflammatory response syndrome (SIRS) to multiple-organ-dysfunction syndrome (MODS). Blood cultures are the current "gold standard" for diagnosis, and they are based on the detection of viable microorganisms present in blood. However, on some occasions, blood cultures have intrinsic limitations in terms of sensitivity and rapidity, and it is not expected that these drawbacks will be overcome by significant improvements in the near future. For these principal reasons, other approaches are therefore needed in association with blood culture to improve the overall diagnostic yield for septic patients. These considerations have represented the rationale for the development of highly sensitive and fast laboratory methods. This review addresses non-culture-based techniques for the diagnosis of sepsis, including molecular and other non-culture-based methods. In particular, the potential clinical role for the sensitive and rapid detection of bacterial and fungal DNA in the development of new diagnostic algorithms is discussed.

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Validation of key behaviourally based mental health diagnoses in administrative data: suicide attempt, alcohol abuse, illicit drug abuse and tobacco use

Hyungjin Myra Kim, Eric G Smith, Claire Stano … (2012)

Background Observational research frequently uses administrative codes for mental health or substance use diagnoses and for important behaviours such as suicide attempts. We sought to validate codes (International Classification of Diseases, 9th edition, clinical modification diagnostic and E-codes) entered in Veterans Health Administration administrative data for patients with depression versus a gold standard of electronic medical record text ("chart notation"). Methods Three random samples of patients were selected, each stratified by geographic region, gender, and year of cohort entry, from a VHA depression treatment cohort from April 1, 1999 to September 30, 2004. The first sample was selected from patients who died by suicide, the second from patients who remained alive on the date of death of suicide cases, and the third from patients with a new start of a commonly used antidepressant medication. Four variables were assessed using administrative codes in the year prior to the index date: suicide attempt, alcohol abuse/dependence, drug abuse/dependence and tobacco use. Results Specificity was high (≥ 90%) for all four administrative codes, regardless of the sample. Sensitivity was ≤75% and was particularly low for suicide attempt (≤ 17%). Positive predictive values for alcohol dependence/abuse and tobacco use were high, but barely better than flipping a coin for illicit drug abuse/dependence. Sensitivity differed across the three samples, but was highest in the suicide death sample. Conclusions Administrative data-based diagnoses among VHA records have high specificity, but low sensitivity. The accuracy level varies by different diagnosis and by different patient subgroup.

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Author and article information

Journal

Journal ID (nlm-ta): BMJ Open

Journal ID (iso-abbrev): BMJ Open

Journal ID (hwp): bmjopen

Journal ID (publisher-id): bmjopen

Title: BMJ Open

Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )

ISSN (Electronic): 2044-6055

Publication date Collection: 2018

Publication date (Electronic): 19 June 2018

Volume: 8

Issue: 6

Electronic Location Identifier: e020857

Affiliations

[1 ] departmentDepartment of Health Policy , Vanderbilt University School of Medicine , Nashville, Tennessee, USA

[2 ] departmentMid-South Geriatric Research Education and Clinical Center , VA Tennessee Valley Health Care System , Nashville, Tennessee, USA

[3 ] departmentDepartment of Pharmacology , Vanderbilt University School of Medicine , Nashville, Tennessee, USA

[4 ] departmentDepartment of Biostatistics , Vanderbilt University School of Medicine , Nashville, Tennessee, USA

Author notes

[Correspondence to ] Dr Andrew D Wiese; andrew.d.wiese.1@ 123456vumc.org

Author information

Andrew D Wiese http://orcid.org/0000-0002-0699-4224

Article

Publisher ID: bmjopen-2017-020857

DOI: 10.1136/bmjopen-2017-020857

PMC ID: 6009457

PubMed ID: 29921683

SO-VID: 7e47bb36-baf3-4695-b25f-331a3b99651c

License:

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

History

Date received : 27 November 2017

Date revision received : 02 May 2018

Date accepted : 11 May 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

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ScienceOpen disciplines: Medicine

Keywords: coding algorithms,medicaid,older adults,serious infections

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ScienceOpen disciplines: Medicine

Keywords: coding algorithms, medicaid, older adults, serious infections

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Validation of discharge diagnosis codes to identify serious infections among middle age and older adults

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Abstract

Objectives

Setting and participants

Design

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Results

Conclusions

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 47

Identifying patients with severe sepsis using administrative claims: patient-level validation of the angus implementation of the international consensus conference definition of severe sepsis.

The era of molecular and other non-culture-based methods in diagnosis of sepsis.

Validation of key behaviourally based mental health diagnoses in administrative data: suicide attempt, alcohol abuse, illicit drug abuse and tobacco use

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Cited by 25

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