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Keywords:

  • cardiovascular disease;
  • length of stay;
  • mortality;
  • prognosis;
  • prognosis score;
  • stroke

Background

Previous prognostic scoring systems in predicting stroke mortality are complex, require multiple measures that vary with time and failed to produce a simple scoring system.

Aims/Hypothesis

The study aims to derive and internally validate a stroke prognostic scoring system to predict early mortality and hospital length of stay.

Methods

Data from a UK multicenter stroke register were examined (1997-2010). Using a prior hypothesis based on our and others observations, we selected five patient-related factors (age, gender, stroke subtype, clinical classification, and prestroke disability) as candidate prognostic indicators. An 8-point score was derived based on multiple logistic regression model using four out of five variables. Performance of the model was assessed by plotting the estimated probability of in-hospital death against the actual probability by testing for overfitting (calibration) and area under the curve methods (discrimination).

Results

The total sample consisted of 12 355 acute stroke patients (ischemic stroke 91·0%). The score predicted both in-patient and seven-day mortality. The crude in-patient mortality were 1·57%, 4·02%, 10·65%, 21·41%, 46·60%, 62·72%, and 75·81% for those who scored 0, 1, 2, 3, 4, 5, and 6, respectively. The calibration of the model revealed no evidence of overfitting (estimated overfitting 0·001). The area under the curve values for both in-hospital and seven-day mortality were 0·79. The score predicted length of stay with a higher score was associated with longer median length of stay in those discharged alive and shorter median length of stay in those who died (P for both <0·001).

Conclusions

A simple 8-point clinical score is highly predictive of acute stroke mortality and length of hospital stay. It could be used as prognostic tool in service planning and also to risk-stratify patients to use these outcomes as markers of stroke care quality across institutions.