Development of a Clinical Prediction Model for In-hospital Mortality from the South African Cohort of the African Surgical Outcomes Study.

BACKGROUND: Data on the factors that influence mortality after surgery in South Africa are scarce, and neither these data nor data on risk-adjusted in-hospital mortality after surgery are routinely collected. Predictors related to the context or setting of surgical care delivery may also provide insight into variation in practice. Variation must be addressed when planning for improvement of risk-adjusted outcomes. Our objective was to identify the factors predicting in-hospital mortality after surgery in South Africa from available data. METHODS: A multivariable logistic regression model was developed to identify predictors of 30-day in-hospital mortality in surgical patients in South Africa. Data from the South African contribution to the African Surgical Outcomes Study were used and included 3800 cases from 51 hospitals. A forward stepwise regression technique was then employed to select for possible predictors prior to model specification. Model performance was evaluated by assessing calibration and discrimination. The South African Surgical Outcomes Study cohort was used to validate the model. RESULTS: Variables found to predict 30-day in-hospital mortality were age, American Society of Anesthesiologists Physical Status category, urgent or emergent surgery, major surgery, and gastrointestinal-, head and neck-, thoracic- and neurosurgery. The area under the receiver operating curve or c-statistic was 0.859 (95% confidence interval: 0.827-0.892) for the full model. Calibration, as assessed using a calibration plot, was acceptable. Performance was similar in the validation cohort as compared to the derivation cohort. CONCLUSION: The prediction model did not include factors that can explain how the context of care influences post-operative mortality in South Africa. It does, however, provide a basis for reporting risk-adjusted perioperative mortality rate in the future, and identifies the types of surgery to be prioritised in quality improvement projects at a local or national level.

A Multicenter, Cross-Sectional Quality Improvement Project: The Perioperative Implementation of a Hypertension Protocol by Anesthesiologists.

BACKGROUND: Hypertension is a common risk factor for cardiovascular morbidity and mortality, with a high prevalence in patients presenting for elective surgery. In limited resource environments, patients have poor access to primary care physicians, limiting the efficacy of lifestyle modification for the management of hypertension. In these circumstances, the perioperative period presents a unique opportunity for diagnosis and initiation and/or modification of pharmacotherapy of hypertension. Anesthesiologists are ideally placed to lead this aspect of perioperative medicine. The study objective was for anesthesiologists to identify patients at the preoperative visit with previously undiagnosed or poorly controlled chronic hypertension and follow a simple management algorithm. METHODS: In collaboration with expert physicians, we designed and implemented an algorithm for the diagnosis and management of chronic hypertension. This was a multicenter, cross-sectional quality improvement project in 7 hospitals in the Western Cape, South Africa. On the day before scheduled elective surgery, adult in-patients had 2 sets of blood pressure (BP) readings taken, one by nurses and the other by anesthesiologists, using a noninvasive automated BP device. These were averaged on an electronic database, to diagnose hypertension. Patients with normal BP or well-controlled hypertension required no further management. Those with borderline BP received educational pamphlets. Patients with stage 1 or 2 hypertension were managed with medication according to the algorithm, starting 1 day postoperatively, and provided with educational pamphlets. Patients with stage 3 disease had their surgery postponed and were referred to a physician. The primary outcome was adherence by the anesthesiologist to the algorithm in the diagnosis and management of hypertension. An 80% adherence rate was considered successful implementation. The secondary outcome was the adherence to the algorithm at discharge. RESULTS: Two hundred ninety-eight patients were screened for hypertension. One hundred six patients were eligible for the quality improvement project. Thirty-seven (34.9%) had borderline BP readings, 43 (40.6%) had stage 1, 22 (20.8%) stage 2, and 4 (3.8%) stage 3 hypertension, respectively. The adherence rate by the anesthesiologist in initiating treatment according to the algorithm was 89 of 106 (84.0%; 95% confidence interval [CI, 77.0-91.0). There was full adherence to the algorithm in 59 of 106 (55.5%; 95% CI, 46.2-65.1) at the time of discharge from hospital. CONCLUSIONS: Anesthesiologists successfully implemented a quality improvement project for diagnosis and management of hypertension in the perioperative period. This has the potential to reduce the public health burden of hypertension in limited resource environments. Successful ongoing prescription and follow-up requires cooperation within a multidisciplinary team.