Objective model using only gender, age and medication list predicts in-hospital morbidity after elective surgery. Blitz JD, Mackersay KS, Miller JC, Kendale SM. British Journal of Anaesthesia 2017;118(4):5444-5550
Presented by: Dr C. Thomas
- Recognised need for objective, customised risk evaluation tool for elective surgery
- For patient and physician
- Aid informed consent
- Improve safety by identification of high risk patients
- Current models require physician input / lab data etc.
- Aim – objective predictor of inpatient post op morbidity
- Simple to use
- Easy to include
- Simple data – age, gender, list of medications
- Gender, age and medication list could provide information about post-operative morbidity
- Certain medications elevate risk
- Simplified to number of medications / gender / age
Design and Setting
- Review board approval – patient consent waived as no intervention mandated
- Restrospective database study
- Single centre
- 2 year period
- Electronic database (Clarity) – access to ICD-9 codes
- ASA scores from anaesthetist at time (database)
- Quaternary Care academic Centre – New York City
- Large inpatient location, ambulatory locations
- Patients with mod – high access to healthcare
- Morbidity outcome was in hospital morbidity by
- Post op complications – presence of any during admission
- AF, PE, MI, VTE, CCF, Resp Failure, AKI
- ICD-9 coding limited – excudes:
- Haemorrhage, sepsis, cardiac arrest
- Secondary database created:
- 46 selected medications – presence or absence each patient (on admission)
- 26629 Adult surgical encounters (>18 years)
- 02% separate patients, 16.98% >1 surgery/patient
- Anaesthesia – GA / Regional / Neuraxial / Monitored anaesthesia care
- Emergency surgery
- No ASA score on database
- Developed predictive models for in hospital morbidity based on outcomes above
- GAMMA – Gender-Age-Medications Morbidity Assessment
- Morbidity based on gender, age and medications
- Logistic regretion based on database
- Morbidity using ASA score as independent variable
- GAMMA-N –GAMMA-Number modification
- Morbidity solely on gender age and number of medications
- Binary logistic regression analyses – assessed for discrimination and power by c-statistic (binary outcomes ie yes or no to condition) – >0.8 indicates strong model.
- Calibration assessed by Brier score (compares actual events with predicted). Score close to 0 suggests accurate.
- Chi-Square for model significance.
- Models developed with full data set and validated with k-fold cross validation – 10 folds.
- Morbidity Risk from gender, age and medications
- Morbidity Risk from ASA score
- Morbidity Risk from gender, age and number of medications
- GAMMA – predicts post operative morbidity with high accuracy (c statistic 0.819, Brier 0.034)
- ASA similar (c-statistic 0.827, Brier score 0.033)
- GAMMA-N less predictive (c-statistic 0.795, Brier 0.050)
- Authors conclude that combination of age, gender and medication list reliably predict post-operative morbidity.
- Model has increased objectivity, can be used pre-operatively (lab values etc not required, different to models such as PPOSSUM)
- Limited medical knowledge required therefore could be patient led.
- Large database
- Authors recognise limitations
- Easy to access data – on the whole not subjective (except ASA)
- Exclusion of haemorrhage, sepsis and cardiac arrest as complications
- Other outcomes that patients would consider as morbidity? – very limited number of outcomes studied
- Patient population – excludes limited resource patients – ? therefore not comparable nationally / internationally or patients not on medications for existing disease due to insurance limitations etc therefore risk may be underscored.
- Limited list of medications included (46) therefore risk may be underscored for patients on less common or new medications etc. How would this be updated with advances in pharmaceuticals?
- Difficult to assess from available information
- If this tool was studied for other populations and proved accurate it could be implemented as a simple risk stratification tool for elective patients but further study would be required.
Potential for impact
- Development of a patient led tool for risk assessment – patient led care
- Pre-operative optimization – reduce their score by improving lifestyle etc to reduce medications
- Risk stratification for allocation of resources? – such as elective joints requiring lowering of BMI before listed for surgery in some areas.