Study of Postoperative Adverse Outcomes of Surgical Patients Using Taiwan National Health Insurance Database

Project: A - Government Institutionb - National Science and Technology Council

Project Details


In contrast with the scoring systems of the American Heart Association, AHA, and New York Heart Association, NYHA, for the medical conditions of cardiac or medical patients, or the scoring system for the critical patients in the intensive care unit, the APACHI score, there were no existing evaluation system in the world for the preoperative quantitative assessment for the surgical patients, except the over-simplified classification by the American Society of Anesthesiology, ASA functional status I-V. There are at least three functional modalities should be taken into consideration when evaluating the potential postoperative adverse outcomes or the final survival for the surgical patients, these are preoperative existing medical conditions, types of anesthesia/surgery, and the postoperative major complications. Although there were numerous studies been done about the outcomes of surgical patients, they are still limited by the population recruitment bias, small sample size and limited scope of investigation. A practical guideline or scoring system, sophisticated, detailed, and quantitative enough to assess the risk for the general surgical population, is still lacking. We have applied the Taiwan National Health Insurance Research Database, NHIRD, to study the comprehensive features of the postoperative adverse outcomes (major complications and mortality) for patients receiving in-hospital surgery in a retrospective, cross-sectional, nation-wide and population-based study, using the reimbursement claims of nearly two millions surgical patients. These reimburse claims for in-patient surgery services include the complete database of medical utility/reimbursement records for prescription, laboratory examination, image studies, procedures/surgery/anesthesia, medical utilities such as nursing care and intensive care surgical patients used. The basic information of hospital and physician was also included. To study the crucial factors affecting the final survival of the surgical patients, the major parameters included the preoperative existing medical conditions, types of surgery or anesthesia, and the 30-day immediate postoperative complications, such as postoperative bleeding, deep wound infection, stroke, myocardial infarction, pneumonia, acute renal failure, sepsis, pulmonary embolism, or mortality rates. Multivariate logistic regression or propensity-score matched-pair method was used to analyze if there were any significant difference between the study group and the control. The aims of this three-year research project include: (I) The first year: disease or surgery-oriented. a. To analyze the comprehensive features of postoperative major complications and mortality for the surgical patients after receiving major surgery and anesthesia. b. To study the impact of common but serious co-existing medical conditions, such as surgical patients receiving renal dialysis or with liver cirrhosis, as well as the rare disease, such as Hemophilia, to the postoperative adverse outcomes. c. To evaluate the potential financial correlation of medical expenditures with the postoperative outcomes. (II) The second year: complication-oriented and relative risk assessment a. Major postoperative complications include stroke, acute myocardial infarction, pneumonia, acute renal failure, and sepsis will be evaluated systematically according to the differences in preoperative medical co-morbidities, types of surgery/anesthesia and other co-existing complications. b. Relative risk assessment with the weighting score of above parameters will be studied. (II) The third year: scoring system and model implementation. a. Data acquisition/collection for the Peri-operative Scoring System for Surgical Patients (PSSSP). b. Implementation/validation of the predictive models for postoperative adverse outcomes in the large-scale database, using artificial neural net work model or traditional multiple logistic regression.
Effective start/end date8/1/157/31/16


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