Defining Multimorbidity in Older Surgical Patients

Bibliographic Data

ID9102832
AuthorsJeffrey H Silber (0000-0001-6415-7910, Center for Outcomes Research, Children’s Hospital of Philadelphia, corresponding author), Joseph G Reiter (0000-0003-3806-4192, Center for Outcomes Research, Children’s Hospital of Philadelphia, corresponding author), Paul R Rosenbaum (0000-0003-0872-1403, Leonard Davis Institute of Health Economics), QINGYUAN ZHAO (0000-0001-9902-2768, Department of Statistics, The Wharton School, The University of Pennsylvania), Dylan S Small (0000-0003-4928-2646, Leonard Davis Institute of Health Economics), Bijan A Niknam (Center for Outcomes Research, Children’s Hospital of Philadelphia, corresponding author), Alexander S Hill (Center for Outcomes Research, Children’s Hospital of Philadelphia), Alex S Hill (0000-0001-7301-5666, Children's Hospital of Philadelphia, corresponding author), Lauren L Hochman (0000-0001-7515-0589, Center for Outcomes Research, Children’s Hospital of Philadelphia, corresponding author), Rachel R Kelz (0000-0001-6635-5961, Leonard Davis Institute of Health Economics), Lee A Fleisher (0000-0003-2899-2294, Department of Anesthesiology and Critical Care, The University of Pennsylvania Perelman School of Medicine, corresponding author)
Year2018
Volume56
Issue8
Pages701-710
Publication date2018-08-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000947
PMID29995695
OpenAlexW2860807556
LanguageEN
Citations received5
References cited43

BACKGROUND: There are numerous definitions of multimorbidity (MM). None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets. OBJECTIVES: Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual qualifying comorbidity set (QCS) more than doubling the odds of mortality versus its reference population. Patients with at least 1 QCS were defined as having MM. RESEARCH DESIGN: Cohort-based study with a matching validation study. SUBJECTS: All fee-for-service Medicare patients between age 65 and 85 without dementia or metastatic solid tumors undergoing general surgery in 2009-2010, and an additional 2011-2013 dataset. MEASURES: 30-day all-location mortality. RESULTS: There were 576 QCSs (2 singles, 63 doubles, and 511 triples), each set more than doubling the odds of dying. In 2011, 36% of eligible patients had MM. As a group, multimorbid patients (mortality rate=7.0%) had a mortality Mantel-Haenszel odds ratio=1.90 (1.77-2.04) versus a reference that included both multimorbid and nonmultimorbid patients (mortality rate=3.3%), and Mantel-Haenszel odds ratio=3.72 (3.51-3.94) versus only nonmultimorbid patients (mortality rate=1.6%). When matching 3151 pairs of multimorbid patients from low-volume hospitals to similar patients in high-volume hospitals, the mortality rates were 6.7% versus 5.2%, respectively (P=0.006). CONCLUSIONS: A list of QCSs identified a third of older patients undergoing general surgery that had greatly elevated mortality. These sets can be used to identify vulnerable patients and the specific combinations of comorbidities that make them susceptible to poor outcomes

Cohort · Cohort study · Comorbidity · Dementia · Disease · Logistic regression · Mortality rate · Odds · Odds ratio · Population · Propensity score matching · Chronic Disease Management Strategies · Emergency Medicine · Frailty in Older Adults · Internal Medicine · Medicine · Sepsis Diagnosis and Treatment

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