Validation and optimisation of an ICD-10-coded case definition for sepsis using administrative health data

Abstract

ObjectiveAdministrative health data are important for health services and outcomes research. We optimised and validated in intensive care unit (ICU) patients an International Classification of Disease (ICD)-coded case definition for sepsis, and compared this with an existing definition. We also assessed the definition's performance in non-ICU (ward) patients.Setting and participantsAll adults (aged ≥18 years) admitted to a multisystem ICU with general medicosurgical ICU care from one of three tertiary care centres in the Calgary region in Alberta, Canada, between 1 January 2009 and 31 December 2012 were included.Research designPatient medical records were randomly selected and linked to the discharge abstract database. In ICU patients, we validated the Canadian Institute for Health Information (CIHI) ICD-10-CA (Canadian Revision)-coded definition for sepsis and severe sepsis against a reference standard medical chart review, and optimised this algorithm through examination of other conditions apparent in sepsis.MeasuresSensitivity (Sn), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) were calculated.ResultsSepsis was present in 604 of 1001 ICU patients (60.4%). The CIHI ICD-10-CA-coded definition for sepsis had Sn (46.4%), Sp (98.7%), PPV (98.2%) and NPV (54.7%); and for severe sepsis had Sn (47.2%), Sp (97.5%), PPV (95.3%) and NPV (63.2%). The optimised ICD-coded algorithm for sepsis increased Sn by 25.5% and NPV by 11.9% with slightly lowered Sp (85.4%) and PPV (88.2%). For severe sepsis both Sn (65.1%) and NPV (70.1%) increased, while Sp (88.2%) and PPV (85.6%) decreased slightly.ConclusionsThis study demonstrates that sepsis is highly undercoded in administrative data, thus under-ascertaining the true incidence of sepsis. The optimised ICD-coded definition has a higher validity with higher Sn and should be preferentially considered if used for surveillance purposes.

Record linkage study of the pathogen‐specific burden of respiratory viruses in children

“…This highlights the limitations of using ICD codes alone when calculating disease estimates, as documented elsewhere . Previous studies also support the notion that coding algorithms, particularly for less well‐defined diseases, are needed for accurate disease estimates . These are crucial to policy development and are often used as baseline data when evaluating the impact of said policies.…”

“…4,17,18 Previous studies also support the notion that coding algorithms, particularly for less welldefined diseases, are needed for accurate disease estimates. 19 These are crucial to policy development and are often used as baseline data when evaluating the impact of said policies. Our findings further highlight the need to look beyond ICD codes when estimating infectious disease burden and the impact of vaccination programs.…”

Validity of administrative data for identification of obstructive sleep apnea

“…Claims-based algorithms have been validated in a number of common disease states (Austin et al, 2002;Chen et al, 2010;Esposito et al, 2015;Hux et al, 2002;Jolley et al, 2015;Kokotailo and Hill, 2005;Kurdyak et al, 2015;Pang et al, 2015;Ronksley et al, 2012;Rule et al, 2009;Tu et al, 2007). Once validated, these algorithms are used frequently to determine the impact of OSA or OSA-related outcomes in population studies, which has important implications for health policy and funding decisions (Alexander et al, 2014;Khazanie et al, 2014;Lenihan et al, 2015;Muntner et al, 2014;Shroff et al, 2014;Wu et al, 2015).…”

“…Current population‐based studies are limited by variable definitions of OSA, the need for specialized testing to confirm the diagnosis and selection bias of the populations under study. These limitations are similar to research on other chronic medical conditions where administrative data algorithms have been studied and, in many cases, validated as a tool for population based assessment (Austin et al ., ; Chen et al ., ; Esposito et al ., ; Hux et al ., ; Jolley et al ., ; Kokotailo and Hill, ; Kurdyak et al ., ; Pang et al ., ; Ronksley et al ., ; Tu et al ., ).…”

Computer-aided National Early Warning Score to predict the risk of sepsis following emergency medical admission to hospital: a model development and external validation study

“…We define sepsis (with at least 1 organ failure or septic shock) 4 based on 84 selected International Classification of Diseases, 10th Revision (ICD-10) codes identified by an optimized validated method reported by Jolley and colleagues in Canada 17 (which we adapted to our study by excluding 6 Canadianspecific ICD-10 codes and 3 procedure codes). We used this optimized approach for identifying sepsis using ICD-10 codes because other methods are known to underestimate sepsis from administrative data.…”