PANSURAS

Perioperative ANesthesia & SURgical Assessment System

An Unanswered Need …

PANSURAS originated in an insight into an evident need for change in the current systems of preoperative surgical risk assessment — a solution eliminating many of the perceptions, deficiencies and lacunae in current preoperative assessment practice.

The need … Perceptions, deficiencies and lacunae

Most physicians at this moment, among which surgeons and anesthesiologists, unknowingly fail to realize the truly vast lack of real knowledge about which risk factors are relevant for each particular type of surgery in their locality. This sounds bombastic and negative, but is generally true. So, what are these perceptions, deficiencies and lacunae needing answers and correction?

  • Nearly all preoperative assessment proceeds according to single clinical belief system. This can be expressed as, “The more comorbidity, the higher the risk.” But is this true for all operations? For example, compare the relevance of multiple comorbidities for a patient requiring an open abdominal aorta reconstruction, versus the same patient requiring a dental filling.
  • Uncertainty as to the relevance of risk factors for specific operations. Examples of risk factors whose individualized relevance is uncertain are heart murmurs, basal crackles, gait, orthopnea, etc. Once again, the same questions of relevance of comorbidities as in the above example.
  • Inappropriate assessment of risk factors. Are risk factors for postoperative outcomes after a specific operation the same as those for a different operation?
  • Cross-cultural inappropriateness. Physicians generalize from risk-factors derived in one part of the world to patients in other parts of the world. But it is very questionable whether risk factors for postoperative outcomes after a specific operation are the same in all parts of the world?
  • Furthermore, if there are some identical risk factors for a postoperative outcome after a specific operation: is the weighting of these risk factors identical in different parts of the world?

The “Global Burden of Disease” studies (See article here) reveal marked differences in comorbidity profiles between different parts of the the world. These will express themselves as differences in risk factors and weightings of the same risk factors for any given surgical procedure. (Click image to enlarge)

  • Inappropriate generalization of modern risk prediction algorithms. A good example is the application of the modern American College of Surgeons risk calculator based upon data from 400+ top-clinical hospitals in the USA (Bilimoria 2013, Irani 2014). This risk calculator is even inaccurate when applied to hospitals other than the 400+ top clinical hospitals in the USA (Adegboyega 2017). Furthermore, the serious question arises whether the same predictive system is relevant in hospitals in other parts of the world, such as: Alice Springs in Australia, or Ahmedabad in India, or Mombasa in Kenya.
  • Inappropriate use of old predictive algorithms. An example is the continued use of the POSSUM score (Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity). This score was first published during 1991 (Copeland 1991), and revised somewhat during 1998 (Prytherch 1998). This predictive and quality system was derived in both cases from clinically admitted English public health system patients who underwent general surgical procedures. Surgical practice and perioperative management have undergone major changes during the last 20 years, so it is doubtful whether this algorithm is even still relevant for English public health system patients, without even considering its relevance in other countries.

PANSURAS … An answer to a need

Modern preoperative assessment is seldom designed for any specific locality, is qualitative and not quantitative, is qualitative, as well as varying somehwat with the physician performing the assessment. This renders understanding of these perceptions, deficiencies and lacunae very relevant indeed. Even so, it does function reasonably well. Nonetheless, it fails to perform as efficiently, or to drive improvements in perioperative quality of care as would a quantitatively based machine-learning system. PANSURAS is just such a machine-lerning system providing quantitative outputs making many improvements in perioperative care possible. So what are the advantages of a system such as PANSURAS?

  • PANSURAS is intranet-based, (i.e. within the intranet of an institution), with a database table structure absolutely identical for all users in every institution where installed. This means that physicians with consulting rooms at various locations, hospitals, regions, and even countries, can readily exchange information with each other, as well as use the same databases. Accordingly an anesthesiologist can assess a patient at one location, and the surgeon sees the results of this assessment at another location. Both can provide extra commentary and data. This interaction integrates by elimination of the “Island Culture” currently existing between physicians practicing different medical specialties, as well as between specialists practising in different hospitals.
  • PANSURAS provides context-related, “on-the-fly” warnings and information pop-ups of potential and real problems. These problems can be recognized and managed preoperatively, and the patient optimized for surgery. It potentially could aid in selecting patients most suited for prehabilitation regimes. Well managed prehabilitation improves the quality of surgical care by reducing the incidence of postoperative complications, shortening hospital length of stay, and improving postoperative wellbeing.
  • The simulation mode of PANSURAS is a useful tool to further investigate the potential effects of preoperative optimization therapies, as well as for teaching. It is a powerful tool for use during multidisciplinary discussions — it facilitates decision-making to determine the most effective preoperative optimization therapy for individual patients.
  • PANSURAS generates statistical risk percentages for mortality and multiple different postoperative problems based upon surgical specialty and type of admission (ambulatory or hospital), as well as the local socioeconomic, demographic, and medical situation. Inbuilt machine-learning modules provide regular recalibration of the coefficients of the predictive equations, so providing surgeons and anesthesiologists with accurate predictions for their local situation anywhere in the world where PANSURAS is installed.
  • Recognition of predicted potential morbidity and mortality enables better planning to be made for postoperative management. So, after operation and recovery, the patient can be discharged to a part of the hospital providing the appropriate level of care. This facilitates the optimal functioning of a “Perioperative Surgical Home”, so enabling significant optimization of resource use in hopsitals. Furthermore, accurate statistical predictions means that operations upon higher-risk patients can be planned so they are more evenly distributed over time, so resulting in more optimal use of intensive care faciltities, as well as bed occupancy in clinical wards. All these things would also potentially eliminate many emergency admissions to the intensive care unit with their associated higher morbidiy, mortality, and costs.
  • PANSURAS includes inbuilt audit and quality control tools with which the functioning of an integrated “Perioperative Surgical Home” can be monitored, and continually improved.
  • The database tables of PANSURAS are identical for each installation. This means that data from multiple institutions can be readily combined for benchmarking of different institutions, raising the real possibility of developing efficient specialist units, or even the designation of “preferred providers” for certain specific disorders.

References:

  1. Adegboyega T.O., et al, (2017), Applying the National Surgical Quality Improvement Program risk calculator to patients undergoing colorectal surgery: theory vs reality. The American Journal of Surgery, 213: 30-35.
  2. Bilimoria K.Y, et al, (2013), Development and Evaluation of the Universal ACS NSQIP Surgical Risk Calculator: A Decision Aid and Informed Consent Tool for Patients and Surgeons. Journal of the American College of Surgeons. 217: 833-842.
  3. Copeland G.P., et al, (1991), POSSUM: a scoring system for surgical audit. British Journal of Surgery, 78: 356-360.
  4. Irani J.L., (2014), Participation in Quality Measurement Nationwide. Clinics in Colon and Rectal Surgery, 27: 14-18.
  5. Prytherch D.R., et al, (1998), POSSUM and Portsmouth POSSUM for predicting mortality. British Journal of Surgery, 85: 1217-1220.


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