The Charlson Comorbidity Index Can Be Used Prospectively to Identify Patients Who Will Incur High Future Costs
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{"title"=>"The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs", "type"=>"journal", "authors"=>[{"first_name"=>"Mary", "last_name"=>"Charlson", "scopus_author_id"=>"7005501957"}, {"first_name"=>"Martin T.", "last_name"=>"Wells", "scopus_author_id"=>"7402075997"}, {"first_name"=>"Ralph", "last_name"=>"Ullman", "scopus_author_id"=>"36605470100"}, {"first_name"=>"Fionnuala", "last_name"=>"King", "scopus_author_id"=>"56440975500"}, {"first_name"=>"Celia", "last_name"=>"Shmukler", "scopus_author_id"=>"6507649642"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "pmid"=>"25469987", "doi"=>"10.1371/journal.pone.0112479", "pui"=>"600684387", "issn"=>"19326203", "sgr"=>"84916239466", "scopus"=>"2-s2.0-84916239466"}, "id"=>"fab1edb0-ca84-3b4a-a8a1-3cbfa36d34c4", "abstract"=>"BACKGROUND: Reducing health care costs requires the ability to identify patients most likely to incur high costs. Our objective was to evaluate the ability of the Charlson comorbidity score to predict the individuals who would incur high costs in the subsequent year and to contrast its predictive ability with other commonly used predictors. METHODS: We contrasted the prior year Charlson comorbidity index, costs, Diagnostic Cost Group (DCG) and hospitalization as predictors of subsequent year costs from claims data of fund that provides comprehensive health benefits to a large union of health care workers. Total costs in the subsequent year was the principal outcome. RESULTS: Of the 181,764 predominantly Black and Latino beneficiaries, 70% were adults (mean age 45.7 years; 62% women). As the comorbidity index increased, total yearly costs increased significantly (P<.001). At lower comorbidity, the costs were similar across different chronic diseases. Using regression to predict total costs, top 5(th) and 10(th) percentile of costs, the comorbidity index, prior costs and DCG achieved almost identical explained variance in both adults and children. CONCLUSIONS AND RELEVANCE: The comorbidity index predicted health costs in the subsequent year, performing as well as prior cost and DCG in identifying those in the top 5% or 10%. The comorbidity index can be used prospectively to identify patients who are likely to incur high costs. TRIAL REGISTRATION: ClinicalTrials.gov NCT01761253", "link"=>"http://www.mendeley.com/research/charlson-comorbidity-index-used-prospectively-identify-patients-incur-high-future-costs", "reader_count"=>86, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Student > Doctoral Student"=>10, "Researcher"=>19, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>7, "Other"=>8, "Student > Master"=>16, "Student > Bachelor"=>7, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Student > Doctoral Student"=>10, "Researcher"=>19, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>7, "Other"=>8, "Student > Master"=>16, "Student > Bachelor"=>7, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Agricultural and Biological Sciences"=>1, "Arts and Humanities"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>3, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>4, "Engineering"=>2, "Nursing and Health Professions"=>7, "Mathematics"=>1, "Medicine and Dentistry"=>51, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>2, "Social Sciences"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>51}, "Social Sciences"=>{"Social Sciences"=>2}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>7}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>7}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>1, "United Kingdom"=>2, "Spain"=>4}, "group_count"=>7}

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  • {"files"=>["https://ndownloader.figshare.com/files/1817365", "https://ndownloader.figshare.com/files/1817368"], "description"=>"<div><p>Background</p><p>Reducing health care costs requires the ability to identify patients most likely to incur high costs. Our objective was to evaluate the ability of the Charlson comorbidity score to predict the individuals who would incur high costs in the subsequent year and to contrast its predictive ability with other commonly used predictors.</p><p>Methods</p><p>We contrasted the prior year Charlson comorbidity index, costs, Diagnostic Cost Group (DCG) and hospitalization as predictors of subsequent year costs from claims data of fund that provides comprehensive health benefits to a large union of health care workers. Total costs in the subsequent year was the principal outcome.</p><p>Results</p><p>Of the 181,764 predominantly Black and Latino beneficiaries, 70% were adults (mean age 45.7 years; 62% women). As the comorbidity index increased, total yearly costs increased significantly (P<.001). At lower comorbidity, the costs were similar across different chronic diseases. Using regression to predict total costs, top 5<sup>th</sup> and 10<sup>th</sup> percentile of costs, the comorbidity index, prior costs and DCG achieved almost identical explained variance in both adults and children.</p><p>Conclusions and Relevance</p><p>The comorbidity index predicted health costs in the subsequent year, performing as well as prior cost and DCG in identifying those in the top 5% or 10%. The comorbidity index can be used prospectively to identify patients who are likely to incur high costs.</p><p>Trial Registration</p><p>ClinicalTrials.gov <a href=\"http://clinicaltrials.gov/show/NCT01761253\" target=\"_blank\">NCT01761253</a></p></div>", "links"=>[], "tags"=>["Charlson comorbidity index", "dcg", "comorbidity index", "Incur High Future Costs BackgroundReducing health care costs", "health care workers", "Diagnostic Cost Group", "age 45.7 years", "year Charlson comorbidity index", "nct", "Charlson comorbidity score", "RelevanceThe comorbidity index"], "article_id"=>1258552, "categories"=>["Biological Sciences"], "users"=>["Mary Charlson", "Martin T. Wells", "Ralph Ullman", "Fionnuala King", "Celia Shmukler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0112479.s001", "https://dx.doi.org/10.1371/journal.pone.0112479.s002"], "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Charlson_Comorbidity_Index_Can_Be_Used_Prospectively_to_Identify_Patients_Who_Will_Incur_High_Future_Costs_/1258552", "title"=>"The Charlson Comorbidity Index Can Be Used Prospectively to Identify Patients Who Will Incur High Future Costs", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-12-03 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1817288"], "description"=>"<p>Yearly cost per person adjusted for age, gender, major mental health diagnoses, and zip code of residence.</p><p>Beneficiaries and 2010 health care costs according to the prior year Charlson comorbidity index.</p>", "links"=>[], "tags"=>["Charlson comorbidity index", "dcg", "comorbidity index", "Incur High Future Costs BackgroundReducing health care costs", "health care workers", "Diagnostic Cost Group", "age 45.7 years", "year Charlson comorbidity index", "nct", "Charlson comorbidity score", "RelevanceThe comorbidity index"], "article_id"=>1258491, "categories"=>["Biological Sciences"], "users"=>["Mary Charlson", "Martin T. Wells", "Ralph Ullman", "Fionnuala King", "Celia Shmukler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112479.t002", "stats"=>{"downloads"=>5, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Beneficiaries_and_2010_health_care_costs_according_to_the_prior_year_Charlson_comorbidity_index_/1258491", "title"=>"Beneficiaries and 2010 health care costs according to the prior year Charlson comorbidity index.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-03 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1817290"], "description"=>"<p><b>The numbers of patients in each comorbidity group are shown on </b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112479#pone-0112479-t001\" target=\"_blank\"><b>Table 1</b></a><b>.</b></p><p>Costs adjusted for age, gender, major mental health diagnoses, and zip code of residence.</p><p>Percent of beneficiaries according to 2010 hospitalizations and average 2010 yearly costs according to prior year comorbidity index.</p>", "links"=>[], "tags"=>["Charlson comorbidity index", "dcg", "comorbidity index", "Incur High Future Costs BackgroundReducing health care costs", "health care workers", "Diagnostic Cost Group", "age 45.7 years", "year Charlson comorbidity index", "nct", "Charlson comorbidity score", "RelevanceThe comorbidity index"], "article_id"=>1258493, "categories"=>["Biological Sciences"], "users"=>["Mary Charlson", "Martin T. Wells", "Ralph Ullman", "Fionnuala King", "Celia Shmukler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112479.t003", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percent_of_beneficiaries_according_to_2010_hospitalizations_and_average_2010_yearly_costs_according_to_prior_year_comorbidity_index_/1258493", "title"=>"Percent of beneficiaries according to 2010 hospitalizations and average 2010 yearly costs according to prior year comorbidity index.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-03 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1817285"], "description"=>"<p>Different weights assigned for specific conditions in the comorbidity index.</p>", "links"=>[], "tags"=>["Charlson comorbidity index", "dcg", "comorbidity index", "Incur High Future Costs BackgroundReducing health care costs", "health care workers", "Diagnostic Cost Group", "age 45.7 years", "year Charlson comorbidity index", "nct", "Charlson comorbidity score", "RelevanceThe comorbidity index"], "article_id"=>1258488, "categories"=>["Biological Sciences"], "users"=>["Mary Charlson", "Martin T. Wells", "Ralph Ullman", "Fionnuala King", "Celia Shmukler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112479.t001", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Different_weights_assigned_for_specific_conditions_in_the_comorbidity_index_/1258488", "title"=>"Different weights assigned for specific conditions in the comorbidity index.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-03 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1817282"], "description"=>"<p>The x-axis is the adjusted comorbidity index found by subtracting the weight of each disease from the patient's comorbidity index <i>for those patients with the stated disease</i>. Thus, a patient with an adjusted comorbidity index of 0 has only that chronic disease.</p>", "links"=>[], "tags"=>["Charlson comorbidity index", "dcg", "comorbidity index", "Incur High Future Costs BackgroundReducing health care costs", "health care workers", "Diagnostic Cost Group", "age 45.7 years", "year Charlson comorbidity index", "nct", "Charlson comorbidity score", "RelevanceThe comorbidity index"], "article_id"=>1258486, "categories"=>["Biological Sciences"], "users"=>["Mary Charlson", "Martin T. Wells", "Ralph Ullman", "Fionnuala King", "Celia Shmukler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112479.g002", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_y_axis_is_total_costs_that_is_the_total_yearly_costs_for_patients_with_that_disease_according_to_the_comorbidity_level_/1258486", "title"=>"The y-axis is total costs, that is, the total yearly costs for patients with that disease according to the comorbidity level.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-03 03:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1817279"], "description"=>"<p>The x-axis is the adjusted comorbidity index found by subtracting the weight of each disease from the patient's comorbidity index <i>for those patients with the stated disease</i>. Thus, a patient with an adjusted comorbidity index of 0 has only that chronic disease.</p>", "links"=>[], "tags"=>["Charlson comorbidity index", "dcg", "comorbidity index", "Incur High Future Costs BackgroundReducing health care costs", "health care workers", "Diagnostic Cost Group", "age 45.7 years", "year Charlson comorbidity index", "nct", "Charlson comorbidity score", "RelevanceThe comorbidity index"], "article_id"=>1258483, "categories"=>["Biological Sciences"], "users"=>["Mary Charlson", "Martin T. Wells", "Ralph Ullman", "Fionnuala King", "Celia Shmukler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112479.g001", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_y_axis_is_the_proportion_of_patients_with_a_given_chronic_disease_according_to_the_adjusted_comorbidity_index_/1258483", "title"=>"The y axis is the proportion of patients with a given chronic disease according to the adjusted comorbidity index.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-03 03:00:00"}
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