Developing and validating a novel multisource comorbidity score from administrative data: a large population-based cohort study from Italy

Abstract

ObjectiveTo develop and validate a novel comorbidity score (multisource comorbidity score (MCS)) predictive of mortality, hospital admissions and healthcare costs using multiple source information from the administrative Italian National Health System (NHS) databases.MethodsAn index of 34 variables (measured from inpatient diagnoses and outpatient drug prescriptions within 2 years before baseline) independently predicting 1-year mortality in a sample of 500 000 individuals aged 50 years or older randomly selected from the NHS beneficiaries of the Italian region of Lombardy (training set) was developed. The corresponding weights were assigned from the regression coefficients of a Weibull survival model. MCS performance was evaluated by using an internal (ie, another sample of 500 000 NHS beneficiaries from Lombardy) and three external (each consisting of 500 000 NHS beneficiaries from Emilia-Romagna, Lazio and Sicily) validation sets. Discriminant power and net reclassification improvement were used to compare MCS performance with that of other comorbidity scores. MCS ability to predict secondary health outcomes (ie, hospital admissions and costs) was also investigated.ResultsPrimary and secondary outcomes progressively increased with increasing MCS value. MCS improved the net 1-year mortality reclassification from 27% (with respect to the Chronic Disease Score) to 69% (with respect to the Elixhauser Index). MCS discrimination performance was similar in the four regions of Italy we tested, the area under the receiver operating characteristic curves (95% CI) being 0.78 (0.77 to 0.79) in Lombardy, 0.78 (0.77 to 0.79) in Emilia-Romagna, 0.77 (0.76 to 0.78) in Lazio and 0.78 (0.77 to 0.79) in Sicily.ConclusionMCS seems better than conventional scores for predicting health outcomes, at least in the general population from Italy. This may offer an improved tool for risk adjustment, policy planning and identifying patients in need of a focused treatment approach in the everyday medical practice.

Measuring multimorbidity beyond counting diseases: systematic review of community and population studies and guide to index choice

“…Twenty articles originated from the United States1718192021222324252627282930313233343536; three from Australia373839; two each from the United Kingdom,4041 Taiwan,4243 and Italy4445; and one each from Canada,46 Spain,47 Germany,48 New Zealand,49 Norway,50 and India 51. They were published between 1968 and 2017, with 15 (43%) published since the last systematic review on this topic in 2009 171819204042434445464748495051. The mean number of participants included in the derivation populations of indices developed after 2009 was 356 906, compared with 75 491 before 2009.…”

“…The mean number of participants included in the derivation populations of indices developed after 2009 was 356 906, compared with 75 491 before 2009. The newer indices primarily required access to medical records in 11 (73%) cases,1819204042434445474849 and the remainder (4, 27%) self-report17465051; 10 (50%) indices before 2009 primarily used medical records21232629323334353639 and 10 (50%) used self-report 22242527283031373841…”

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

“…The RxRiskV Index was improved for our study to include updated ATC codes for medications licensed in Italy currently and adding the pertaining ICD-9 CM code for each condition. These amendments were made according to previously published works [22][23][24][25][26][27][28][29]. Individuals were classified as having one of the conditions listed if they received at least ≥2 consecutive dispensations of a drug for treatment of a specific class of disease and/or one hospital discharge with the diagnoses coded with the specific ICD-9-CM (S2 Table).…”

“…Categorization was made by assigning increasing values of 1, 2, 3 and 4 to 0, 1-4, 5-9 and ≥10 drugs (comedication score) and 1, 2, 3 and 4 to 0, 1-2 and ≥3 comorbidities (comorbidity score). In addition, cases and controls were categorized according to the Multisource Comorbidity Score (MCS), a new index of patients' clinical status derived from inpatients diagnostic information and outpatient drug prescriptions provided by the regional Italian data and validated for outcome prediction [22,33]. To simplify comparisons, the original five categories of worsening clinical profile (0, 1, 2, 3 and 4) as defined by MCS, were reduced to milder (MCS=0), middle (1MCS3) and severe (MCS≥4) categories.…”

Effectiveness and Costs Associated to Adding Cetuximab or Bevacizumab to Chemotherapy as Initial Treatment in Metastatic Colorectal Cancer: Results from the Observational FABIO Project

“…Baseline covariates included age, sex, year of mCRC diagnosis and surgery in the timespan between the dates of colorectal cancer diagnosis and treatment start. In addition, the so-called Multisource Comorbidity Score (MCS), a simple score recently developed and validated in Italy [15], was used for assessing the general clinical profile of each cohort member. In the current study, the weights of the conditions that contribute to the score were recalculated by considering the cohort of cancer patients, rather than the general population as in the original version of the MCS [16].…”