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Elective surgery

Myocardial injury after surgery. Can dabigatrin reduce the risk?

Dabigatran in patients with myocardial injury after non-cardiac surgery (MANAGE): an international, randomised, placebo-controlled trial

Lancet 2018;391:2325-34 doi:10.1016/S0140-6736(18)30832-8

Presented by: Dr James Lloyd

Background

  • Myocardial injury after non-cardiac surgery (MINS) is a relatively newly described diagnosis, having only been described about 4 years ago
  • The diagnosis includes myocardial infarction and isolated ischaemic troponin elevation occurring within 30 days after surgery, but does not include perioperative myocardial injury due to non-ischaemic causes for example caused by sepsis, or rapid heart rate.
  • It is common, estimated at around 8 million cases internationally every year, and carries an increased risk of mortality.
  • This is the first randomised controlled trial to address this group of patients.

Design & Setting

  • A multicentre, international, placebo controlled randomised control trial involving 1754 patients

Subjects

  • Patients over 45, who had received non cardiac surgery, and were within 35 days of what the authors term MINS (Myocardial Injury after Non cardiac Surgery).
  • MINS is defined as having either elevated troponin with ischaemic signs or symptoms, ischaemic electrocardiographic changes, or new or presumed new ischaemic abnormality on cardiac imaging or an isolated elevated troponin measurement without an alternative explanation.

Intervention

  • Patients were assigned either the intervention arm which was 110mg of Dabigatran twice daily for up to two years, or matched placebo.
  • Patients were then also randomised to receive either 20mg of omeprazole once daily, or placebo, as part of a separate study.

Outcomes

  • The primary outcome was a major vascular complication (This was defined as a composite of vascular mortality, and non-fatal myocardial infarction, non-haemorrhagic stroke, peripheral arterial thrombosis, amputation, and symptomatic venous thromboembolism).
  • The primary safety outcome, to monitor the side effect profile of the intervention, were a composite of life-threatening, major, and critical organ bleeding.

Results

  • A major vascular complication occurred in 97 (11%) of 877 patients allocated to dabigatran and in 133 (15%) of 877 patients allocated to placebo (HR 0·72, 95% CI 0·55–0·93, p=0·0115)
  • In addition, Dabigatran did not increase the risk of life-threatening, major, or critical organ bleeding (primary safety outcome) compared with placebo (HR 0·92, 95% CI 0·55–1·53, p=0·78)
  • In the analysis of the secondary outcomes Dabigatran was shown to increase the risk of minor bleeding, clinically non-significant lower gastrointestinal bleeding, and dyspepsia.
  • The subgroup analysis shows that the hazard ratios are not statistically significant for patients receiving dual antiplatelet therapy, or for patients who showed only an isolated troponin rise.

Conclusions

  • In patients identified as having MINS, instigating a treatment of 110mg BD dabigatran reduced the probability of a major vascular complication, without increasing the risk of major, or life threatening bleeding.
  • The number needed to treat with dabigatran to prevent one major vascular complication was 24, whereas the number needed to harm (i.e. to cause major, rather than life threatening bleeding) is 54.
  • The subgroup analysis fails to reinforce the concept of routine monitoring of troponin, as the patients identified only by a troponin rise did not show any benefit from the intervention.

Strengths

  • A large, well blinded randomised control trial, with good length of follow up

Weaknesses

  • Trial terminated early due to slow recruitment and withdrawal of funding
  • Because of the early termination the primary outcome measures for the trial were altered part way through
  • The subgroup analysis shows benefit for one group, but not another, suggesting two different sets of pathology

Implications

  • That unless routine screening of perioperative troponin levels is instigated 90% of MINS events will be missed.
  • That treating these patients with BD Dabigatran 110mg will reduce vascular complications.

Potential for impact

I don’t think this is really very clear, for two main reasons;

  1. Although the subgroup of patients who had ischaemic changes on ECG showed benefit from the treatment, they also had quite low levels of treatment with traditional secondary prevention medication, for which there is already lots of evidence.
  2. The group of patients who were only identified with troponin changes did not show any benefit from the intervention, which means there is little point starting this costly screening process when the authors are keen to point out that this is the only trial investigating the treatment of this group of patients.

 

 

Personalised prehabilitation….the future?

Personalised Prehabilitation in High-risk Patients Undergoing Elective Major Abdominal Surgery. A Randomised Blinded Controlled Trial.

 Annals of Surgery 2018;267(1):50-56 doi:10.1097/SLA.0000000000002293

Presented by: Rebeca Harris ST4

Background

  • Major abdominal surgery is associated with a high rate of postoperative complications, particularly in elderly patients with multiple comorbidities.
  • Aerobic capacity determines postoperative functional reserve, which is negatively associated with postoperative morbidity and mortality.
  • Prehabilitation exercise programmes are postulated to improve aerobic capacity, and thereby reduce postoperative complications.
  • Previous studies have shown a bias towards low-risk patients, and lack of evidence on postoperative clinical outcomes.
  • Prehabilitation is defined as a preparatory intervention aiming to increase aerobic capacity. Methods include supervised endurance exercise training and the promotion of physical activity.

Design & Setting

  • Single centre: Hospital Clinic de Barcelona
  • Patients were blindly randomized
  • Collaborating anaesthetists and surgeons were blinded to patient’s allocation
  • Ethics approved
  • Sample size prospectively powered (as standard, accepting risks of: α 0.05 and β 0.2), based on:
    • the reduction rate of patients with postoperative complications as the main outcome
    • considering the local colorectal complication rate (30%), and
    • anticipating up to 20% drop out
    • Intention-to-treat analysis

Subjects

High risk patients for elective major abdominal surgery

Inclusion criteria:

  • Elective major abdominal surgery
  • High risk defined by all of the following:
    • Age > 70 and/or ASA III/IV
    • Duke Activity Status Index Score < 46
    • Preop schedule allowed at least 4 weeks for the prehabilitation intervention

Exclusion criteria:

  • Non-elective surgery
  • Unstable cardiorespiratory disease
  • Locomotor limitations precluding exercise training
  • Cognitive deterioration impeding adherence to the programme

Intervention

  • Baseline assessment within 1 week of preoperative assessment
  • Reassessment 1 week before surgery

Standard care:

  • Physical activity, nutritional and smoking cessation advice
  • IV iron if indicated for anaemia
  • Nutritional intervention if high-risk for malnutrition

Intervention:

  • Personalised prehabilitation programme based on health and social circumstances
  • Mostly community based
  • 3 major aspects
    • Motivational interview to assess adherence profile. Tailored physical activity programme then co-designed with the patient
    • Personalised daily physical activity programme
      • Pedometer to measure steps, then feedback and optimization
    • Supervised high intensity endurance exercise programme
      • 1-3 per week
      • Exercise bike interval training, tailored to increase intensity over time, based on work rate
      • Pulse oximetry and self-perceived exertion measured

Outcomes

Primary

  • Number of patients with a complication

Secondary

  • Number and severity of postoperative complications
  • Hospital and ICU length of stay

Other

  • Endurance time
  • Distance covered in 6 minute walking test
  • Physical activity (by validated patient survey)
  • Self perceived health status (by validated patient survey)
  • Psychological status (by HADS patient survey)
  • Pulmonary function tests
  • Cardiorespiratory exercise tests

 Results

Baseline characteristics

  • 209 assessed over 3 years – 144 eligible and randomised (> 70 per group, as per power analysis minimum)
  • Comparable patient characteristics between groups
  • 19 did not receive operation, so excluded mid-trial
  • Control: 1 unable to perform exercise testing, 6 abandoned
  • Intervention: 4 unable to perform exercise testing, 4 abandoned
  • 56 (Control) and 54 (Intervention) completed trial (< 70 per group, and > 20% dropout rate, thus underpowered)

 Control

  • No change in baseline characteristics at start vs 1 week pre-surgery

 Prehabilitation intervention

  • Mean duration 6 weeks + 12 supervised exercise session
  • PRMIARY OUTCOME
    • 50% reduction in number of patients with complications: 31% vs 62%, RR 0.5 (95% CI 0.3-0.8), p = 0.001
  • SECONDARY OUTCOMES:
    • Increasein Endurance Time (135%, p < 0.001)
    • Increasein Physical Activity Index (37 points, p< 0.001)
    • No significant difference in intraoperative parameters, but trend towards lower requirement of vasoactive drugs (p=0.053)
    • Lower mean number of complications per patient : Cardiovascular(p = 0.03, RR 0.1, 95% CI 0.1-1.0), Infection of uncertain source (p = 0.013, RR not possible), Paralytic ileus (p = 0.001, RR not possible)
    • In patients with complications, intervention reducedrisk of having more than one complication (RR 0.6, but 95% CI 0.3-1.1), but no effect on severity of complications.
    • Reduced length of ICU stay (3 vs 12 days, p = 0.046)

 Conclusions

  • High intensity endurance exercise training is feasible and safe in elderly and/or multimorbid candidates for major abdominal surgery
  • Prehabilitation enhanced clinical outcomes in high-risk candidates for elective major abdominal surgery, which can be explained by the increase in aerobic capacity
  1. Reduced complication rate
  2. Prevents > 1 complication
  3. Reduced ICU length of stay

Strengths

  • Randomised blinded controlled trial
  • High risk patient group selected, reflecting patient population
  • Initially adequately powered
  • Performed within realistic preoperative timeframe for urgent surgery
  • Highly personalised, patient-centred prehabilitation programme. Well detailed for reproducibility
  • Interesting secondary outcomes, validated tools used
  • Appropriate statistical analysis employed
  • Number of patients abandoning intervention arm < control, thus patient engagement good

Weaknesses

  • Single centre
  • Blinding of clinicians following interaction with patients may have been difficult
  • Underpowered following dropouts – still able to demonstrate statistically significant difference in primary outcome, however may have ‘missed’ other significant differences in secondary outcomes
  • ? Blinding of exercise tester
  • Primary outcome extremely broad – ‘any complications’ postoperatively. Not specific, and therefore clinical significance and importance of question reduced.
  • Survival and functional recovery not assessed
  • Underpowered to assess effect on specific and important post operative complications
  • Did not demonstrate a difference in the severity of postoperative complications
  • Claims significant reduction in CV complications and the number of patients having > 1 complication, but:
    • Reduction in CV complications 95% CI 0.1-1, wide and includes ‘no effect’
    • Reduction in number of patients having more than 1 complication 95% CI 0.3-1.1, wide and includes ‘no effect’
  • Endurance time rather than familiar, objective CPEX data e.g. Anabolic threshold, formed basis of measure of aerobic capacity
  • Intensive, highly tailored programme
    • ? Sustainability on larger scale
    • No cost analysis

Implications

  • Prehabilitation in elderly, multimorbid patients appears to be feasible, safe and ‘acceptable’ to patients
  • Prehabilitation can increase preoperative exercise endurance in high risk patients
  • Postoperative complication rates can be reduced by this strategy
  • However, larger trials are required to further characterise and assess the clinical significance of postoperative complication benefits, and to determine the effect on functional recovery and survival

Potential for impact

  • Important step towards assessing the potential benefits and characterising the design of prehabilitation exercise programmes in high risk patients undergoing high risk elective surgery
  • Important and exciting emerging field of research with potential for significantly improving patient outcomes

 

 

 

SORT: A new tool to predict postoperative morbidity.

Predicting postoperative morbidity in adult elective surgical patients using the Surgical Outcome Risk Tool (SORT). Wong DJN, Oliver CM, Moonesinghe SR.

British Journal of Anaesthesia 2017;119(1):95-105 doi: 10.1093/bja/aex117

Presented by: Dr Alex Cormack

Background

  • Perioperative risk assessment is a key part of the consent process
  • Risk stratification tools also allow comparison between outcomes of different institutions
  • Morbidity following surgery can have a significant impact on quality of life and needs to be a consideration when considering surgical options
  • Morbidity is more common than mortality following surgery and potentially provides a more sensitive measure of comparison between different healthcare providers
  • P-POSSUM and POSSUM are currently the most frequently used tools for perioperative risk prediction

POSSUM:

  • Physiological and Operative Severity Score for the enumeration of Mortality and morbidity
  • Developed in the 1990s
  • For use in elective and emergency general surgical procedures
  • Does not apply to trauma patients
  • Calculated at the time the decision to operate is made
  • Variants include CR-POSSUM, Vascular-POSSUM and O-POSSUM
  • Requires 12 physiological and 6 operative parameters to calculate

P-POSSUM:

  • Portsmouth modification of the Physiological and Operative Severity Score for the enumeration of Mortality and morbidity.
  • A variation of the POSSUM tool

SORT:

  • Surgical Outcome Risk Tool
  • Developed after the 2011 NECEPOD report
  • Uses six parameters collected preoperatively
  • Designed to predict probability of 30 day mortality following surgery
  • The authors state that it ‘compared favourably with other previously validated risk stratification tools’ and ‘has been externally validated recently in a cohort of patients undergoing hip fracture surgery’.
  • Predictor variables: ASA grade (III, IV or V), surgical urgency (expedited, urgent or immediate), high risk specialities (GI, thoracic or vascular surgery), surgical severity (major or complex), malignancy, age (65-79 or >80)

 Design and Setting

  • Single centre prospective study at University College London Hospital to:

“develop and validate a new model to predict the likelihood of postoperative                       morbidity using predictor variables found in SORT, and then compare its      performance against POSSUM.”

  • 3 year period (June 2009 – May 2012)
  • Data collection carried out by trained research staff independent of the clinical teams responsible for the patient

Subjects

Inclusion criteria:

  • Patients undergoing elective major inpatient operations
  • 1934 patients included

Exclusion criteria:

  • Patients with duplicated or missing data
  • Patients who did not have POMS (Post Operative Morbidity Survey) scores recorded on Day 7

Intervention

  • Data collected:
    • 1934 patients identified, 1583 patients included
    • 58% female
    • 45% orthopaedic and 39% abdominal procedures
    • 6 deaths within 30 days of surgery
  • Data excluded:
    • 351 patients excluded
    • Missing predictor variables: DOB, ASA status, surgical speciality, malignancy status
    • Missing POMS outcomes: duplicated or missing entries
    • Clear summary of reasons for exclusion, no patients unaccounted for
  • POMS administered prospectively to patients at several time points postoperatively (day 3,5,7 or 8, 14 or 15 and 21)
  • Morbidity outcome measure selected was POMS-defined morbidity recorded after postoperative day 7 or 8
  • Data randomly split into two groups
    • 1/3 validation group (n=527)
    • 2/3 derivation group to define new model (n=1056)

Outcomes

  • Predictor variables from the original SORT variables were adjusted to generate SORT-morbidity models
  • Outcome variable was set as the presence of POMS defined morbidity on postoperative day 7 or 8
  • SORT-morbidity models then tested in the validation group using statistical analysis
  • Final model then tested against POSSUM

Results

  • No statistically significant difference between new SORT-morbidity model and POSSUM at discrimination of morbidity at 7 days post surgery
  • Linear shrinkage factors estimated to improve prediction of morbidity at later time points

Conclusions

  • New SORT-morbidity model is comparable to POSSUM at prediction of morbidity 1 week post operatively
  • Linear shrinkage factors can be applied to improve morbidity prediction further in the postoperative course

Strengths

  • Morbidity is an important consideration within the surgical consent process
  • Data collection carried out by research staff independent of clinical teams
  • Clear documentation of reasons for exclusion
  • Good number of data sets
  • New SORT-morbidity model found to compare favourably to POSSUM
  • POMS is a validated measure of morbidity to use in data collection

Weaknesses

  • Morbidity defined as POMS defined morbidity after day 7
    • Patients discharged prior to this time period excluded despite possible morbidity
  • Only looked at elective patients
  • Single centre study
  • Unequal representation of surgical specialities
  • Comparatively low mortality rate (0.31%) documented – the authors comment that rates of 0.37-0.67% have been documented elsewhere in the literature
  • POMS domains include some relatively minor measures of morbidity that may influence the results (for example urinary catheter following elective urology cases)
  • Required ‘linear shrinkage factors’ to enable morbidity to be predicted later than 7 days postoperatively

Implications and Potential for Impact

  • Possible development of a new tool to use alongside existing risk assessment tools
  • SORT-morbidity was only used in elective cases and therefore could not be used in a CEPOD setting without further studies
  • Potential for further studies to develop the SORT-morbidity tool for more widespread use
  • P-POSSUM currently universally understood amongst the theatre MDT whereas SORT is less widely understood
  • SORT and SORT-morbidity require fewer variables to calculate, however this is less relevant as SORT-morbidity has only been developed using elective cases

The use of SORT-morbidity as an alternative to P-POSSUM does not yet seem a realistic prospect. P-POSSUM is understood amongst surgical and anaesthetic professionals and allows management decisions to be made appropriately. It is used for both emergency and elective patients, and arguably its most important use is in planning the management of emergency patients. This is an area that the SORT-morbidity tool has not been developed for. Further studies and multi-centre validation would be required before it could reliably be used in clinical practice.

 

 

The SLUScore……..

The SLUScore: A Novel Method for Detecting Hazardous Hypotension in Adult Patients Undergoing Noncardiac Surgical Procedures. 

Anesthesia and Analgesia 2017;124(4):1135–1152. 

Presented by: Dr T. Newton

Background

  • Adequate blood pressure control is one of the major concerns in an intra-operative setting.
  • Increasing evidence that extended periods of severe hypotension may effect long-term outcomes.
  • Patients currently categorised in binary fashion- intervention either unnecessary or already too late.
  • Hypothesis: adverse outcomes affected by severity of hypotension and duration accumulated below thresholds commonly encountered during anaesthesia.

Design and Setting

  • 3 centre retrospective cohort study
  • Approval from institutional ethics bodies
  • Databases searched for adult patients undergoing non-cardiac procedures. Data collected on demographics, Charlson comorbidity score, type of anaesthetic, case duration, blood loss, minute to minute MAP values, all-cause 30 day mortality.
  • Development of score:
    • N=33904
    • Multivariable logistic regression to identify risks associated with increased 30 day mortality including time spent below 31 commonly encountered MAP thresholds
    • % increase in odds of 30d mortality calculated for each minute spent below each of the MAP thresholds
    • Number of minutes calculated for each threshold required to produce identical increases in 30d mortality from 5-30%
    • 20% set used to determine SLUScore- each increment of score corresponds to +5%compounding progression of odds of 30d mortality
  • Validation of score with 3 centre study, n=116,541

Results

  • Independent factors affecting 30 day mortality: age, Charlson comorbidity score, cumulative blood loss. All adjusted for.
  • Dropping below progressively lower MAP thresholds à greater increase in 30d mortality per unit time below that threshld.
  • Preoperative diagnosis of hypertension means time needed below each threshold for same increase in risk.
  • Increase in mortality depended on number of exposure limits exceeded.
  • 30 day mortality approximately doubled in patients with SLUScore >0.
  • Less time may be spent at lower MAP to accrue same risks (analogous to diving charts).

Strengths

  • Large sample size for creation of score, larger size across multiple sites for valifation.
  • 5 year duration at one site.
  • Accounts for some confounding factors, separate scores for pre-existing hypertension.

Weaknesses

  • Does not account for severity/risk of procedure- increased risk of procedure vs MAP, or does procedure cause drop in MAP?
  • Minute to minute measurements taken from extrapolation of 5 minute NIBPs.

Impact

  • Too complex to calculate in real time intra-op.
  • Relies on assumptions with 5 minute NIBPs in most cases.
  • May be useful in future generations of monitors/anaesthetic machines as in-built function.
  • Potential for litigation- if any patient has morbidity from renal function/sepsis etc and has had a GA, anaesthetist may be targeted using SLUScore.

Is postoperative delirium associated with cognitive decline?

Postoperative delirium in elderly patients is associated with subsequent cognitive impairment. Sprung J, Roberts R, Weingarten T et al.

British Journal of Anaesthesia 2017;119(2):316-323

Presented by: Dr I. Roberts

Background  

Paper examined the risk for postoperative delirium (POD) in patients with mild cognitive impairment (MCI) or dementia, and the association between POD and subsequent development of MCI or dementia in cognitively normal elderly patients.

Design & Setting

  • Ran by Mayo clinic.
  • Made use of a cohort that already available on a database to ascertain if patients with cognitive dysfunction experienced post operative delirium.
  • Patients 65 yr of age enrolled in the Mayo Clinic Study of Aging who were exposed to any type of anaesthesia from 2004 to 2014 were included.
  • Cognitive status was evaluated before and after surgery by neuropsychological testing and clinical assessment, and was defined as normal or MCI/dementia.
  • Postoperative delirium was detected with the Confusion Assessment Method for the intensive care unit.

 Subjects

  • In 2004, 70–89 yr olds were identified from a Mayo Clinic Database, randomly selected, and invited to participate in the study.
  • In 2008, ongoing recruitment was initiated using the same protocols as baseline
  • In 2012, the lower limit of the age criterion was reduced to 50 years of age.
  • The study includes all participants enrolled and examined in person in the MCSA study from November 2004 to February 2014 who underwent surgeries and procedures under anaesthesia at Mayo Clinic in Rochester, MN, USA
  • Only participants who were 65 yr of age at enrolment were included.

 Intervention

  • Nil specific intervention.
  • The use of the CAM ICU scoring system was used to identify post op delirium.
  • A pre and post op cognitive impairment screen was used to ascertain both baseline and post op cognition levels.

Outcomes

Claimed to have confirmed previous findings that in a general surgical population elderly patients with cognitive dysfunction at the time of surgery are at higher risk for clinically evident post op delirium compared with patients without mild cognitive impairment.

Main finding is that elderly patients who are cognitively normal at a detailed assessment performed before surgery and who experience clinically evident post op delirium are more likely to develop cognitive impairment or dementia subsequently compared with those who do not experience post op delirium.

Results

  • The frequency of POD was higher in patients with pre-existing cognitive impairment compared with no cognitive impairment/dementia.
  • The frequency of MCI/dementia at the first postoperative evaluation was higher in patients who experienced POD compared with those who did not.

Conclusions

Mild cognitive impairment or dementia is a risk for post op delirium. Elderly patients who have not been diagnosed with cognitive impairment but experience post op delirium are more likely to be diagnosed subsequently with cognitive impairment or dementia.

Strengths

  • Ambitious project.
  • Made us of a large source of data that was readily available.
  • Appeared to confirm a notion that is already in existing literature.
  • Made use of validated means for detecting both cognitive dysfunction and post op delirium.

Weaknesses

  • Observational study only.
  • Mixed methodology with observation both retrospective and prospective.
  • Data observational in nature with no firm validated intervention being studied.
  • Lots of the conclusions seem to be inferred.
  • Did not drill into the data to ascertain if there was one particular factor causing post op delirium.
  • Did not offer any insights into how this phenomenon may be tackled in the future.
  • The journal club group felt it was a weak paper with no rigorous methodology that could be used to garner meaningful results.
  • Appears that this group had a large data set and used it to contrived this paper, which doesn’t appear to clinically add anything to this known phenomenon that already exists in the literature.

Implications

The reader will be aware of the notion that the long term effect of anaesthesia can result in cognitive impairment. The reader will also be appreciative of the fact that post op delirium can contribute to cognitive decline in at risk patients over time.

Potential for impact

  • Raises awareness amongst trainees about this phenomenon.
  • Impact level is poor due to weaknesses within the paper.

 

Predicting morbidity after elective surgery……is there an easy method?

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

Background

  • 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
  • Hypothesis:
  • 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)

Subjects

  • 26629 Adult surgical encounters (>18 years)
  • 02% separate patients, 16.98% >1 surgery/patient
  • Anaesthesia – GA / Regional / Neuraxial / Monitored anaesthesia care
  • Exclusions
    • Emergency surgery
    • No ASA score on database

Intervention

  • 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
  • ASA-M
    • 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.

Outcomes

  • Morbidity Risk from gender, age and medications
  • Morbidity Risk from ASA score
  • Morbidity Risk from gender, age and number of medications

Results

  • 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)

Conclusions

  • 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.

Strengths

  • Large database
  • Authors recognise limitations
  • Easy to access data – on the whole not subjective (except ASA)

Weaknesses

  • 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?

Implications

  • 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.

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