title: Adverse outcome in surgery for chronic leg ischaemia--risk factors and risk prediction when using different statistical methods.
abstract: OBJECTIVE
  to compare two different statistical methods in predicting the outcome of surgery for chronic leg ischaemia.
  
  
  METHODS
  the present study from the Swedvasc registry is based on an inception cohort of 1635 patients with chronic leg ischaemia (intermittent claudication in 609 and critical ischaemia in 1026 patients), who have been followed until 1 year after surgery. Outcome was classified as improved vs. not improved, amputated or dead in claudication and as the intact leg vs. amputation or death in critical ischaemia.
  
  
  METHODS
  logistic regression analysis was compared to the inductive expert system program, Assistant Professional, in the prediction of outcome. Seventy per cent of cases in the data base were used to create a risk factor model including 17 variables registered in Swedvasc. These variables included an assessment of patients overall health status, severity of disease, the surgeon's experience and surgical procedures. This model was then evaluated using the remaining 30% of the patients in the data base.
  
  
  RESULTS
  a risk score indicating a probability of an adverse outcome exceeding 0.5 was, in patients with intermittent claudication, associated with a sensitivity of 38% using logistic regression and 26% using Assistant Professional. The percentages of correctly predicted adverse outcomes were 29 and 50%, respectively. In patients with critical ischaemia, the sensitivities with the two methods were 68 and 38% and the predictive values 42 and 57%, respectively.
  
  
  CONCLUSIONS
  the risk scores created with the two methods gave low sensitivities. It is concluded that risk functions could not be used to predict an adverse outcome in patients operated on for chronic leg ischaemia with the data set used.
authors.affiliation: Department of Surgery, Central Hospital, Karlskrona, Sweden.
authors.forename: T
authors.initials: T
authors.lastname: Troëng
authors.forename: L
authors.initials: L
authors.lastname: Janzon
authors.forename: D
authors.initials: D
authors.lastname: Bergqvist
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000671:Amputation, Surgical / Q000706:statistics & numerical data; D002908:Chronic Disease; D003627:Data Interpretation, Statistical; D005103:Expert Systems; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007511:Ischemia / Q000401:mortality / Q000601:surgery*; D007866:Leg / Q000098:blood supply*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications / Q000401:mortality / Q000601:surgery*; D011446:Prospective Studies; D012044:Regression Analysis; D012086:Reoperation; D012306:Risk; D016019:Survival Analysis