Statistical Process Improvement
Georgetown University
 

Risk Assessment


 

Assigned Reading

This section of the course is a review of material you have had in an introductory course on statistics

  • Risk assessment (use instructor's last name as password) Read►

Presentations

  1. Lecture on risk assessment
  2. Scoring for Multi-Morbidity Indices  Video► Slides►
  3. Tree diagrams & Bayes’ theorem  Slides►  Video►  You Tube►
  4. Calculation of sensitivity, specificity, and Area under the Receiver Operating Curves Excel► Slides► Video►

Assignments

Question 1: Construct a simple Multi-Morbidity Index.  Assess the average severity of CHF, MI, Diabetes, Hypertension, Alcohol Use, and ACL surgery (assume that sicker patients have longer stays). To calculate the average severity associated with a disease, compare all cases with the disease to all control patients without the disease. Make sure that in each comparison, patients with and without disease have the same set of comorbidities. For example, to find the average length of stay for patients with MI, select all MI patients with the following comorbidities: CHF,DM,AA.  Then compare these cases to controls who do not have MI but have the same comorbidities. To help you understand this assignment, consider the following table.  In this table, we see different strata of mutually exclusive and exhaustive comorbidities.  Then, within each strata we can observe the impact of MI.  The impact of MI is the average impact of MI within each strata.  Your objective is to create this table before you calculate the impact of MI.  To do so, first estimate n1 through n5 through a file where the data is restricted to cases with MI (WHERE MI=1).  Then, estimate the values n6 through n10 through a file where the data are restricted to non-MI patients (WHERE MI=0).  Merge these data, making sure that you match on the strata.  Then you can calculate the impact of MI.  Data►  Video► Slides►  SQL Code► Answer►

Strata Cases of MI Controls with No MI Difference
AA n1 n6 n1-n6
AA, CHF n2 n7 n2-n7
AA,DM n3 n8 n3-n8
DM, CHF n4 n9 n4-n9
AA,DM,CHF n5 n10 n5-n10

Question 2:  In question 1, calculate the likelihood ratio associated with each diagnosis in predicting above or below average length of stay.  First calculate the weighted average length of stay for each diagnosis.  Assign individuals who have above weighted average length of stay 1 and those below 0.  Next calculate the likelihood ratio (remembering to weight each strata by the count of patients that fall into each strata).  To calculate the likelihood ratio, select all individuals who have above weighted average length of stay.  Examine the prevalence of the diagnosis among them.  Select all individuals who have below weighted average length of stay and select the prevalence of the diagnosis among them.  The ratio of these two calculated numbers constitutes the likelihood ratio. Enter the likelihood ratios calculated for each diagnosis into a Table called #LR. Data►  SQL► Kanfer's Teach One►

ICD9 Likelihood Ratio
AA  
CHF  
DM  
MI 3.47

Question 3:  Using the Table #LR (see question 2), calculate the probability of long stay for the 3 patients described below. 

ID ICD9
1 AA
1 DM
1 HIV
2 MI
2 CHF
3 DM
3 CHF
3 AA
3 MI

Question 4: Calculate the average age and the likelihood ratio associated with diagnoses. You would need to use SQL to do this assignment.  You can use any SQL software, including Access. Since the data is massive (17 million rows), keep in mind that Access requires you to analyze the data in partitions. Microsoft SQL server can analyze the entire data in one run. Submit your SQL code and the 10 diagnosis with highest and lowest likelihood ratios. For password to access data contact your instructor.  By opening this file you agree not to share the file with anyone else. Massive Data► Access► SQL Code► Marla's Guide► Answer► Manalac's Teach One►

Question 5: Calculate the Receiver Operating Characteristic curve associated with predicting from age whether the patient will live or die.  The data do not provide a predicted values, but you can construct a prediction based on age exceeding the cutoff value.  If the patient's age exceeds the cutoff value, then we would predict that the patient will die.  For example, the cutoff values for the ages provided can be 40, 50, 60, 70, 80, 90.  If we take the cutoff value 60, then we predict that all patients above 60 will die and all patients below 60 will live.  Calculate sensitivity and specificity for all cutoffs.  Then, draw the Receiver Operating Characteristic curve.  At what age cutoff the sum of the sensitivity and specificity is at its maximum? Data►

   Mid-Point of Age Range Total 
45 55 65 75 85
True Condition  Alive  33 30 35 25 20 143
Dead  3 5 7 15 33 63
Total  36 35 42 40 53 206

Question 6: Calculate the Receiver Operating Characteristic curve for male and female ages in the attached data set. The field "Alive" contains whether the patient was alive in 6 months post assessment  date.  Alive is shown with 0 and dead with 1.  Age at start of data collection is provided in the field "Age."  Patients' age at assessment must be calculated from age at start and additional days till assessment. The field "DaysFirst" indicates number of days since recording of the age.  The field "Sex" indicates whether male (M) or female (F).  Data► SQL► Answer►

More

  • Introduction to Standard Query Language  More►
  • A comparative study of various severity measures. PubMed►
  • Use of severity of illness to classify and monitor medication errors More►
  • Use of pharmacy data to measure severity of illness More  
  • Risk of risk assessment More►
  • Nursing severity indices More►
  • Community wide measures of quality of care More►
  • Measures of outpatient severity of illness More►
  • Severity of episodes of illness More►
  • Disease Staging More►
  • Patient Management Categories More►
  • The Acute Physiological and Chronic Health Evaluation (APACHE) index More►
  • Medisgroup More►
  • Computerized Severity Index More►
  • Indices that rely on select categories of diagnoses  Charlson Index► Elixhauser List►
  • Comorbidity-Polypharmacy Score More►

 

 
Copyright 1996 Farrokh Alemi, Ph.D. Created on Sunday, October 06, 1996 4:20:30 PM Most recent revision 10/03/2018.  This page is part of the course on Quality, the lecture on Risk Assessment.