To minimize the potential of missing data, patients who missed their appointment were contacted a new follow up appointment was set up. This is a self- completed questionnaire evaluating the disease activity parameters, functional ability as well as quality of life. Prior to baseline examination in the clinic and every follow-up appointment, each patient completed a patient reported outcome measures (PROMs) questionnaire. Patients with past history of cancer or hepatitis, HIV or any other contraindication to DMARDs therapy. Patients taking oral steroids for non-arthritic/ other medical causes.ģ. Patients with history of RA, urogenital, intestinal or other forms of infection.Ģ. Results: PsA patients who had higher incidence of comorbid condition and were at high risk of hospitalization were men, with older age at disease onset, high BMI (p 6 weeks, or remittent pain involving any finger and/or toe for 3 months. Internal and external validation were carried out. A weighted index that was developed in a cohort of 1707 PsA patients. Outcomes of interest included functional ability, quality of life, medications induced complications, hospitalization/death. Methods: This was a retrospective multicenter cohort analysis of PsA patients in a rheumatology clinical registry, assessing the effect of different comorbidities measured at patients’ visits over 10-years period. 2.develop and validate a prospectively applicable comorbidity index for classifying PsA patients according to their comorbid conditions. identify comorbidities with greatest impact on PsA patients’ health status. Given appropriate arguments, the generate_comorbidity_df function will use the parallel backend provided by foreach to improve performance.Objective: 1. This makes the function quite fast for large data sets. This function only considers each ICD-9-CM code once and then merges the resulting comorbidity flags together for each patient. Kable( generate_comorbidity_df(cases, icd9mapfn=icd9cm_charlson_quan)) id This function also includes an optimization from Van Walraven that reduces dmcx to dm if the specific diabetic complication is separately coded. The above process is encapsulated in a single function generate_comorbidity_df. Here’s what happens when that code is passed to a few of the mapping functions listed above:Ĭases <- vt_inp_sample cases_with_cm <- merge(cases, icd9cm_charlson_quan( levels(cases $icd9cm)), by.x= "icd9cm", by.y= "row.names", all.x= TRUE) # generate crude comorbidity summary for each patient kable( ddply(cases_with_cm. 2005).įor example, the #5 ICD-9-CM code above is D25000, or “250.00”, which is for “Diabetes Mellitus Unspecified Type”. 1999) set of categories using a method published by Boersma (Boersma et al. “RCRI” is the Revised Cardiac Risk Index (Lee et al. “Quan” refers to the same paper by Quan mentioned above. “AHRQ37” is an adapation of the AHRQ version 37 software (Agency for Healthcare Research & Quality 2013). “Elixhauser” refers to the Elixhauser comorbidity map, which is a more detailed list than Charlson. 2005) refer to the primary authors of different methods of determining Charlson comorbidities from ICD-9-CM codes. The names “Deyo” (Deyo, Cherkin, and Ciol 1992), “Romano” (Romano, Roos, and Jollis 1993), and “Quan” (Quan et al. “Charlson” refers to the Charlson Comorbidity Index (Charlson et al. The package includes a set of mapping functions that transform a list of ICD-9-CM codes into a comorbidity matrix: In the meantime, there is a wealth of administrative data available within the ICD-9-CM diagnostic and procedural codes stored within US healthcare systems. It is likely that “dual coding” of claims in both sets will continue for some time. ICD-9-CM is updated annually.Īt some point, perhaps as soon as October 2015, ICD-10-CM codes will need to be used instead. National Center for Health Statistics (NCHS) developed ICD-9-CM, which has been required for Medicare and Medicaid claims since 1979. ICD-9-CM is an adaptation of the venerable ICD-9 standard which was developed in 1978. The coding system currently in use is ICD-9-CM. In the United States, the records for every inpatient and outpatient encounter is reviewed by a qualified medical coder who assigns a set of diagnosis and procedural codes based on phrases within the medical record. Medical chart abstraction just isn’t feasible for projects of this scale. The routines in the medicalrisk package (McCormick and Joseph 2015) are designed to help determine comorbidity and medical risk status of a given patient using several popular models published in the peer-reviewed literature.Īdministrative healthcare data is frequently the only available source for determining individual risk of mortality when looking at thousands or millions of patient records.
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