Problem 5. An important problem in emergency medicine is the prioritization of high-risk patients. Tradi- tional triage algorithms classify patients into categories based on vital signs [such as heart rate and level of consciousness} in addition to the patient's reason for seeking medical care: red [life- threatening}, orange [seously ill}, yellow {ill}, green [needs assessment], and blue [minor com- plaints}. However, studies suggest that this System may suffer from low specicity, such that too many patients who are actually at low risk are sorted into high-risk categories; this can lead to increased waiting times for patients who are in urgent need of care. Recent studies have attempted to improve traditional triage algorithms. One study was conducted in Norway to investigate whether information from a set of routine blood tests administered to almost all patients admitted to an emergency department could improve the prediction of more tality risk. The primary outcome in the study was 30-day mortality; i.e., death within 39 days of admission to the emergency department. The eight blood tests examined were c-reactive protein, potassium, sodium. hemoglobin, creatinine. leukocyte count. albumin level. and lactate dehydro- genase; the results of these tests are typically available within 15 minutes. Data from 4,545 individuals with complete observations are in mrt.Rdata; individuals classi- fied as blue (minor complaints} were excluded from the study sample. The descriptions of the variables are as follows. Variable Descri tion triage triage rating upon admission to the emergency department age age, age in years. rounded to the lowest integer sex sex. coded female and mate crp c-reactive protein level in nmoli' L k potassium level in mmulf L as sodium level in mmoli' L hb hemoglobin level in mmol! L cre creatinine level in Isunoli' L leu leukocyte count in 109 cells! L alb albumin level in 3:1 ldh lactate dehydrogenase level in units! L mortJE coded 1 if died within 30 days, 6 otherwise Use these data to answer the following questions. a} For some blood test measures, both low and high values may indicate higher risk of mor- tality. Briey explain why using such a measure as a predictor for mortality risk could po- Use these data to answer the following questions. a} For some blood test measures, both low and high values may indicate higher risk of mor- tality. Briey explain why using such a measure as a predictor for mortality risk could po- 4 tentially introduce a modeling issue and deseribe a possible method for handling the issue based on ideas discussed in the course. Note: Proceed with the rest of the problem without implementing the described method. b} Conduct a formal analysis investigating whether there is an association between triage clas- sification and 30-day mortality. Summarize the results in language accessible to someone who has not taken a statistics course. Be sure to check any necessary assumptions. c} Fit a model estimating the association between 39-day mortality and triage classication, adjusting for age and sex. i. ii. iii. iv. Interpret the model coefcient for age. Interpret the model coefcient for triagegreen. Compare the estimated odds of 30-day mortality for a 55-year-old female categoriesed as orange to those of a 7D-yeanold male categoriZed as yellow. Are either more likely to die within 30 days than survive? Explain your answer. ls this model a better parsimonious model than a model with only triage classication as a predictor? Explain your