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How should I prepare for modeling?
Published in Thomas A. Gerds, Michael W. Kattan, Medical Risk Prediction, 2021
Thomas A. Gerds, Michael W. Kattan
For all categorical variables, one should record the possible categories and, when necessary, an additional explanation. For example, a variable called “sex” could have possible categories “0” and “1” where the additional explanation is that “0” means “female” and “1” means “male.” A categorical variable which can take on many values may need to be adapted to the sample size and number of events. For example, the potential range of values of the Charlson comorbidity index can be 0 to 37 [130]. However, when for example only a few patients in the purpose dataset had a Charlson comorbidity score above 3, then it can be necessary to collapse this variable into fewer categories and for example, define three levels: . These modifications can all be performed using basic commands in most statistical software packages.
Surgical Strategy for Spinal Metastases *
Published in Alexander R. Vaccaro, Charles G. Fisher, Jefferson R. Wilson, 50 Landmark Papers, 2018
Bryan Rynearson, Malcolm Dombrowski, Joon Lee
One significant limitation of this study is that it fails to incorporate patient comorbidities into the treatment algorithm. A comorbidity index, for example, the Charlson Comorbidity Index,1 could prove a salient additional factor to further delineate treatment groups. Since inclusion and exclusion criteria were not stated in the study, it is unclear how patients deemed too sick for surgery owing to comorbid conditions were managed or if and how this treatment system can be applied in these patients. This may affect the applicability of this system; therefore, careful consideration must be taken on a patient-by-patient basis. Another notable limitation was that patient prognostic scores were not strictly adhered to with regard to the recommended surgical intervention. For example, 5 of the 11 patients that received decompression and stabilization surgery had scores of 8 or greater, for which this system recommends conservative palliative management. Numerous patient and surgeon-specific factors likely account for this discordance; however, the authors fail to acknowledge these discrepancies and how it could affect the interpretation of their results. Another important potential limitation is the failure to report the impact that radiotherapy and chemotherapy have on patient survival. It is not clear which patients had tumors sensitive to these adjunctive therapies and how many received these treatments in each of the four groups. The authors acknowledge the importance of these therapies on patient survival, but no discussion is offered explaining their rationale for excluding them from the scoring system.
Cancer
Published in Rachael E. Docking, Jennifer Stock, International Handbook of Positive Aging, 2017
Michelle Lycke, Lies Pottel, Tom Boterberg, Supriya G. Mohile, Etienne Brain, Philip R. Debruyne
The relative incidence of co-morbidities increases with age. Research has shown an association between the presence of co-morbidities and the older cancer patient’s prognosis (Mohile and Magnuson, 2013). Further, it is stated that concomitant diseases not only impact survival but that they also may impact the behaviour of the cancer itself. Therefore, screening for co-morbidities forms an essential part of the CGA (Extermann and Hurria, 2007). Co-morbidities can be assessed through the Charlson Comorbidity Index which includes 19 diseases weighted from one to six points (Charlson et al., 1987) or through the Cumulative Illness Rating Scale for Geriatrics (Miller and Tower, 1991).
Cohort profile: Nordic Helicobacter Pylori eradication project (NordHePEP)
Published in Scandinavian Journal of Gastroenterology, 2023
Anna-Klara Pettersson, Giola Santoni, Jacinth Yan, Cecilia Radkiewicz, Shaohua Xie, Helgi Birgisson, Eivind Ness-Jensen, My von Euler-Chelpin, Joonas H. Kauppila, Jesper Lagergren
Table 2 presents some baseline characteristics of the cohort participants. The largest number of participants were recruited from Finland (40.2%), followed by Sweden (28.5%), Denmark (21.5%), Norway (8.6%), and Iceland (1.2%). The median age at inclusion was 57 years (interquartile range 43–69 years) and 54.3% were women. Comorbidity was assessed using the latest version of the well-validated Charlson comorbidity index for patient registries (Supplementary Table S4) [39]. To reflect the comorbidity burden at the time of cohort entry, we included diagnoses recorded within 5 years of inclusion or set to missing if less than 5 years of data were available in the patient registries. A majority of cohort participants (73.7%) had a Charlson comorbidity index score of 0. Missing data on comorbidity (3.7%) were largely due to the late start of the Norwegian Patient Registry (2008) compared to the Norwegian Drug Registry (2004). The proportions of unsuccessful HP treatment or re-infection with HP were 6.2% and 6.9%, respectively.
Client and service factors associated with changes in health-related quality of life following community rehabilitation
Published in Disability and Rehabilitation, 2023
David A. Snowdon, Scott McGill, Christie Altmann, Kathryn Brooks, Tori Everard, Kate Le Fevre, Nadine E. Andrew
Covariates used in our analysis were extracted from clients’ electronic medical records using a structured data extraction tool and included demographic, clinical and health service factors. Co-morbidities were derived from medical records to calculate the Charlson Comorbidity Index; a single weighted score used to predict 10-year mortality [21]. Higher Charlson Comorbidity Index indicates greater comorbid status. Socioeconomic status was estimated using the Index of Relative Socioeconomic Advantage Disadvantage (IRSAD) divided into predetermined quintiles obtained from the Australian Bureau of Statistics [22]. The IRSAD is calculated based on suburb of residence (i.e., post codes) using national census data on people and households, including education, occupation, living conditions, and income. Higher IRSAD scores indicate lesser relative disadvantage.
Determinants of acute care discharge in adults with chronic obstructive pulmonary disease
Published in Physiotherapy Theory and Practice, 2023
Shweta Gore, Jennifer Blackwood, Houser Emily, Fernandez Natalia
A total of 888 adults (age 55+) with a COPD diagnosis were admitted to the hospital and, of these, 535 had complete basic mobility or daily activity “6-clicks” data. Of these, 441 (82.4%) received physical therapy and 211 (38.1%) received occupational therapy. Figure 1 describes how the final sample was selected for this study. The majority of the sample was white (87.9%), male (52.0%), with an average age of 69.46 years (SD = 8.71). The mean length of stay was 6.31 (SD = 4.73) days. Although over 91.3% were admitted from home, 38.7% were discharged to home, self-care and 34.8% were discharged home with home health services, while 26.5% were admitted to an inpatient rehabilitation facility. Medicare was the primary insurance for 59.8% of the sample. The average Charlson Comorbidity Index score was 4.66 (SD = 1.28) with a range from 3 to 10. Demographic information can be found in Table 1.