Reliability of comorbidity scores derived from administrative data in the tertiary hospital intensive care setting: a cross-sectional study

Li, Michael Hua-Gen; Hutchinson, Ana; Tacey, Mark; Duke, Graeme

doi:10.1136/bmjhci-2019-000016

Cited by 19 publications

(20 citation statements)

References 42 publications

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“…Addition of comorbidities to the candidate predictors included in the model did not improve performance of the model (Supplementary Table 1). Since comorbidities are known to be under-reported during acute presentations, 4 and they offered no improvement on model performance, models without comorbidities were preferred.…”

Section: Resultsmentioning

confidence: 99%

“…More recent models have since been developed, 2,3 some of which overcome a number of these limitations, including the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) model and corresponding (simplified) 4C score, which was developed in a UK secondary care population representing 260 hospitals in England, Scotland and Wales (the ISARIC dataset). 3 Whilst the 4C score showed reasonable discrimination for mortality, there are a number of limitations, including a reliance on clinicians counting specific comorbidities, which are known to be under-recorded in secondary care, 4 and an absence of imaging data among the candidate predictors. Furthermore, it is unclear if this model can help with predicting deteriorating patients beyond day of admission.…”

Section: Introductionmentioning

confidence: 99%

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Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

Adderley

Taverner

Price

et al. 2021

Preprint

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ObjectivesExisting UK prognostic models for patients admitted to hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score.DesignCandidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection.SettingModel development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset.ParticipantsPatients with COVID-19 admitted to UHB January-August 2020 were included.Main outcome measuresDeath and ITU admission within 28 days of admission.Results1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786).ConclusionsThe novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patient’s probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.Article SummaryStrengths and limitations of this studyWe developed novel prognostic models predicting mortality and ITU admission within 28 days of admission for patients hospitalised with COVID-19, using a large routinely collected dataset gathered at admission with a wide range of possible predictors (demographic variables, symptoms, physiological measures, imaging, laboratory test results).These novel models showed good discrimination and calibration in both derivation and external validation cohorts, and outperformed the existing ISARIC model and 4C score in the derivation dataset. We found that addition of comorbidities to the set of candidate predictors included in model derivation did not improve model performance.If integrated into hospital electronic medical records systems, the model algorithms will provide a predicted probability of mortality or ITU admission for each patient based on their individual data at, or close to, the time of admission, which will support clinicians’ decision making with regard to appropriate patient care pathways and triage. This information might also assist clinicians in explaining complex prognostic assessments and decisions to patients and their relatives.A limitation of the study was that in the external validation cohort we were unable to examine all of the predictors included in the original full UHB model due to only a reduced set of candidate predictors being available in CovidCollab. Nevertheless, the reduced model performed well and the results suggest it may be applicable in a wide range of datasets where only a reduced set of predictor variables is available.Furthermore, it was not possible to carry out stratified analysis by ethnicity as the UHB dataset contained too few patients in most of the strata, and no ethnicity data was available in the CovidCollab dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

Adderley

Taverner

Price

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We also followed the ICD-10 coding algorithm for defining Elixhauser’s comorbidity set by the ICD-9-CM coding system, which was developed by Quan et al [ 25 ]. ECM demonstrated excellent external discrimination for in-hospital mortality of all-causes [ 26 ] and reasonable inter-rater agreement (kappa (κ) coefficient ≥ 0.4) for cardiorespiratory and oncological comorbidities [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Physical Comorbidity According to Diagnoses and Sex among Psychiatric Inpatients in South Korea

Park

Kim

2021

IJERPH

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People with mental disorders are susceptible to physical comorbidities. Mind–body interventions are important for improving health outcomes. We examined the prevalence of physical comorbidities and their differences by diagnoses and sex among psychiatric inpatients. The dataset, from National Health Insurance claims data, included 48,902 adult inpatients admitted to psychiatric wards for at least 2 days in 2016 treated for schizophrenia, schizotypal and delusional disorders, or mood disorders. We identified 26 physical comorbidities using the Elixhauser comorbidity measure. Among schizophrenia-related disorders, other neurological disorders were most common, then liver disease and chronic pulmonary disease. Among mood disorders, liver disease was most common, then uncomplicated hypertension and chronic pulmonary disease. Most comorbid physical diseases (except other neurological disorders) were more prevalent in mood disorders than schizophrenia-related disorders. Male and female patients with schizophrenia-related disorders showed similar comorbidity prevalence patterns by sex. Among patients with mood disorders, liver disease was most prevalent in males and third-most in females. In both diagnostic groups, liver disease and uncomplicated diabetes mellitus were more prevalent in males, and hypothyroidism in females. Mental health professionals should refer to a specialist to manage physical diseases via early assessments and optimal interventions for physical comorbidities in psychiatric patients.

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“… 26 Although we relied mainly on physician-reported diagnosis of HLP with potential provider-reported bias, cardiovascular comorbidities are generally considered as most reliably coded conditions in administrative data. 27 LDL-C was measured indirectly by the Friedewald method. 28 Published reports confirmed that lipid panel measured during the first 24 hours after an acute cardiovascular event reliably represents baseline level.…”

Section: Methodsmentioning

confidence: 99%

Hyperlipidaemia and mortality among patients hospitalised with pneumonia: retrospective cohort and propensity score matched study

et al. 2021

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ObjectiveTo characterise the potential association of hyperlipidaemia (HLP) versus no HLP with all-cause mortality among patients hospitalised for pneumonia.DesignPropensity score matched retrospective study.ParticipantsThe study cohort consisted of consecutive 8553 adults hospitalised at a large academic centre with a discharge diagnosis of pneumonia from 1996 through 2015, followed until death or end of the study period, 17 August 2017.OutcomesThe outcome was HR for mortality at 28 days and in the long term in patients with pneumonia with concurrent HLP compared with those with no HLP. We first constructed multivariable Cox proportional regression models to estimate the association between concurrent HLP versus no HLP and mortality after pneumonia hospitalisation for the entire cohort. We then identified 1879 patients with pneumonia with concurrent HLP and propensity score matched in a 1:1 ratio to 1879 patients with no HLP to minimise the imbalance from measured covariates for further analysis.ResultsAmong 8553 unmatched patients with pneumonia, concurrent HLP versus no HLP was independently associated with lower mortality at 28 days (HR 0.52, 95% CI 0.41 to 0.66) and at a median follow-up of 3.9 years (HR 0.75, 95% CI 0.70 to 0.80). The risk difference in mortality was consistent between 1879 propensity score matched pairs both at 28 days (HR 0.65, 95% CI 0.49 to 0.86) and at a median follow-up of 4 years (HR 0.88, 95% CI 0.81 to 0.96). In the subgroup of patients with clinically measured low-density lipoprotein cholesterol (LDL-C), graded inverse associations between LDL-C levels and mortality were found in both unmatched and matched cohorts.ConclusionsAmong hospitalised patients with pneumonia, a diagnosis of HLP is protective against both short-term and long-term risk of death after adjustment for other major contributors to mortality in both unmatched and propensity score matched cohorts. These findings should be further investigated.

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Reliability of comorbidity scores derived from administrative data in the tertiary hospital intensive care setting: a cross-sectional study

Cited by 19 publications

References 42 publications

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

Physical Comorbidity According to Diagnoses and Sex among Psychiatric Inpatients in South Korea

Hyperlipidaemia and mortality among patients hospitalised with pneumonia: retrospective cohort and propensity score matched study

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