Richard Trafford Spence

A global agenda for electronic injury surveillance: consensus statement from the Trauma Association of Canada, the Trauma Society of South Africa, and the Panamerican Trauma Society

Eiman Zargaran, MD, MHSc, Lauren Adolph, Nadine Schuurman, PhD, Larissa Roux, MD, PhD, Damon Ramsey, MD, Richard Simons, MB, BChir, Richard Spence, MD, Andrew J. Nicol, MD, PhD, Pradeep Navsaria, MD, Juan Carlos Puyana, MD, Neil Parry, MD, Lynne Moore, PhD, Michel Aboutanos, MD, MPH, Natalie Yanchar, MD, Tarek Razek, MD, Chad G. Ball, MD, MSc, and S. Morad Hameed, MD, MPH, for the Trauma Association of Canada, the Trauma Society of South Africa, and the Panamerican Trauma Society

Mobile health technology transforms injury severity scoring in South Africa

BACKGROUNDnThe burden of data collection associated with injury severity scoring has limited its application in areas of the world with the highest incidence of trauma.nnnMATERIAL AND METHODSnSince January 2014, electronic records (electronic Trauma Health Records [eTHRs]) replaced all handwritten records at the Groote Schuur Hospital Trauma Unit in South Africa. Data fields required for Glasgow Coma Scale, Revised Trauma Score, Kampala Trauma Score, Injury Severity Score (ISS), and Trauma Score-Injury Severity Score calculations are now prospectively collected. Fifteen months after implementation of eTHR, the injury severity scores were compared as predictors of mortality on three accounts: (1) ability to discriminate (area under receiver operating curve, ROC); (2) ability to calibrate (observed versus expected ratio, O/E); and (3) feasibility of data collection (rate of missing data).nnnRESULTSnA total of 7460 admissions were recorded by eTHR from April 1, 2014 to July 7, 2015, including 770 severely injured patients (ISS > 15) and 950 operations. The mean age was 33.3xa0y (range 13-94), 77.6% were male, and the mechanism of injury was penetrating in 39.3% of cases. The cohort experienced a mortality rate of 2.5%. Patient reserve predictors required by the scores were 98.7% complete, physiological injury predictors were 95.1% complete, and anatomic injury predictors were 86.9% complete. The discrimination and calibration of Trauma Score-Injury Severity Score was superior for all admissions (ROC 0.9591 and O/E 1.01) and operatively managed patients (ROC 0.8427 and O/E 0.79). In the severely injured cohort, the discriminatory ability of Revised Trauma Score was superior (ROC 0.8315), but no score provided adequate calibration.nnnCONCLUSIONSnEmerging mobile health technology enables reliable and sustainable injury severity scoring in a high-volume trauma center in South Africa.

Collaboration is key to strengthening surgical research capacity in sub-Saharan Africa.

The paucity of research in areas of greatest clinical need must be addressed urgently. We propose a model of collaboration in an era of information systems and emerging mobile health technology that has had significant success across the UK and has shown early encouraging results in South Africa (SA). We foresee that recent examples of surgical research collaboratives in SA will continue to promote regional, national and international hub-and-spoke models and ultimately increase the South-South collaboration that is urgently needed to diffuse the skills and knowledge required to address the unmet surgical need in sub-Saharan Africa.

Injury Severity Score coding: Data analyst v. emerging m-health technology

BACKGROUNDnThe cost of Abbreviated Injury Scale (AIS) coding has limited its utility in areas of the world with the highest incidence of trauma. We hypothesised that emerging mobile health (m-health) technology could offer a cost-effective alternative to the current gold-standard AIS mechanism in a high-volume trauma centre in South Africa.nnnMETHODSnA prospectively collected sample of consecutive patients admitted following a traumatic injury that required an operation during a 1-month period was selected for the study. AISs and Injury Severity Scores (ISSs) were generated by clinician-entered data using an m-health application (ISS eTHR) as well as by a team of AIS coders at Vancouver General Hospital, Canada (ISS VGH). Rater agreements for ISSs were analysed using Bland-Altman plots with 95% limits of agreement (LoA) and kappa statistics of the ISSs grouped into ordinal categories. Reliability was analysed using a two-way mixed-model intraclass correlation coefficient (ICC). Calibration and discrimination of univariate logistic regression models built to predict in-hospital complications using ISSs coded by the two methods were also compared.nnnRESULTSnFifty-seven patients were managed operatively during the study period. The mean age of the cohort was 27.2 years (range 14 - 62), and 96.3% were male. The mechanism of injury was penetrating in 93.4% of cases, of which 52.8% were gunshot injuries. The LoA fell within -8.6 - 9.4. The mean ISS difference was 0.4 (95% CI -0.8 - 1.6). The kappa statistic was 0.53. The ICC of the individual ISS was 0.88 (95% CI 0.81 - 0.93) and the categorical ISS was 0.81 (95% CI 0.68 - 0.87). Model performance to predict in-hospital complications using either the ISS eTHR or the ISS VGH was equivalent.nnnCONCLUSIONSnISSs calculated by the eTHR and gold-standard coding were comparable. Emerging m-health technology provides a cost-effective alternative for injury severity scoring.

A multicentre evaluation of emergency abdominal surgery in South Africa: Results from the GlobalSurg-1 South Africa study

BACKGROUNDnGlobalSurg-1 was a multicentre, international, prospective cohort study conducted to address the global lack of surgicalxa0outcomes data. Six South African (SA) hospitals participated in the landmark surgical outcomes study. In this subsequent study, we collated the data from these six local participants and hypothesised that the location of surgery was an independent risk factor for an adverse outcome following emergency intraperitoneal surgery.nnnMETHODSnParticipating hospitals contributed 30-day outcomes data of consecutive emergency intraperitoneal surgical operations performed during a 2-week period between July and November 2014. The six heterogeneous hospital cohorts were compared by categorical confounders. The primary outcome measure was in-hospital mortality; secondary outcome measures were in-hospital morbidity and length of stay of >14 days. The unadjusted association between hospital and adverse outcome and the univariate association between categorical confounders and adverse outcome were tested. Significant associations were further tested by a multivariate stepwise forward logistic regression model built for each outcome of interest.nnnRESULTSnSix hospitals (designated 1 - 6) contributed outcomes data for 169 operations. The mean age of the patients was 34.9 years (range 9 - 82), 116 (68.6%) were male, and the majority (37.2%) presented as a result of trauma. Hospital 5 was associated with 76-fold increased odds of in-hospital death and 58-fold increased odds of a major in-hospital complication, and hospital 3 was associated with 3-fold increased odds of any in-hospital complication. The final model predicting in-hospital death had a receiver operating characteristic curve statistic of 0.8892.nnnCONCLUSIONnThe hospital is an independent risk factor for risk-adjusted adverse outcomes following emergency intraperitoneal surgery in SA.

Derivation, Validation and Application of a Pragmatic Risk Prediction Index for Benchmarking of Surgical Outcomes

AbstractBackgroundnDespite the existence of multiple validated risk assessment and quality benchmarking tools in surgery, their utility outside of high-income countries is limited. We sought to derive, validate and apply a scoring system that is both (1) feasible, and (2) reliably predicts mortality in a middle-income country (MIC) context.MethodsnA 5-step methodology was used: (1) development of a de novo surgical outcomes database modeled around the American College of Surgeons’ National Surgical Quality Improvement Program (ACS-NSQIP) in South Africa (SA dataset), (2) use of the resultant data to identify all predictors of in-hospital death with more than 90% capture indicating feasibility of collection, (3) use these predictors to derive and validate an integer-based score that reliably predicts in-hospital death in the 2012 ACS-NSQIP, (4) apply the score in the original SA dataset and demonstrate its performance, (5) identify threshold cutoffs of the score to prompt action and drive quality improvement.ResultsnFollowing step one-three above, the 13 point Codman’s score was derived and validated on 211,737 and 109,079 patients, respectively, and includes: age 65 (1), partially or completely dependent functional status (1), preoperative transfusions ≥4 units (1), emergency operation (2), sepsis or septic shock (2) American Society of Anesthesia score ≥3 (3) and operative procedure (1–3). Application of the score to 373 patients in the SA dataset showed good discrimination and calibration to predict an in-hospital death. A Codman Score of 8 is an optimal cutoff point for defining expected and unexpected deaths.ConclusionWe have designed a novel risk prediction score specific for a MIC context. The Codman Score can prove useful for both (1) preoperative decision-making and (2) benchmarking the quality of surgical care in MIC’s.

An Online Tool for Global Benchmarking of Risk-Adjusted Surgical Outcomes

BackgroundIncreasing evidence demonstrates significant variation in adverse outcomes following surgery between countries. In order to better quantify these variations, we hypothesize that freely available online risk calculators can be used as a tool to generate global benchmarking of risk-adjusted surgical outcomes.MethodsThis is a prospective cohort study conducted at an academic teaching hospital in South Africa (GSH). Consecutive adult patients undergoing major general or vascular surgery who met the ACS-NSQIP inclusion criteria for a 3-month period were included. Data variables required by the ACS risk calculator were prospectively collected, and patients were followed for 30xa0days post-surgery for the occurrence of endpoints. Calculating observed-to-expected ratios for ten outcome measures of interest generated risk-adjusted outcomes benchmarked against the ACS-NSQIP consortium.ResultsA total of 373 major general and vascular surgery procedures met the inclusion criteria. The GSH operative cohort varied significantly compared to the 2012 ACS-NSQIP database. The risk-adjusted O/E ratios were significant for any complication O/E 1.91 (95xa0% CI 1.57–2.31), surgical site infections O/E 4.76 (95xa0% CI 3.71–6.01), renal failure O/E 3.29 (95xa0% CI 1.50–6.24), death O/E 3.43 (95xa0% CI 2.19–5.11), and total length of stay (LOS) O/E 3.43 (95xa0% CI 2.19–5.11).ConclusionFreely available online risk calculators can be utilized as tools for global benchmarking of risk-adjusted surgical outcomes.

Comparative assessment of in-hospital trauma mortality at a South African trauma center and matched patients treated in the United States

Background: The unacceptably high rate of death and disability due to injury in Sub‐Saharan Africa is alarming. The objective of this work was to compare mortality rates between severely injured trauma patients at a high‐volume trauma center in South Africa with matched patients in the United States. Methods: Clinical databases from the Groote Schuur Hospital for patients treated in Cape Town, South Africa and the American College of Surgeons National Trauma Databank for patients treated at large academic trauma centers in the United States were used. Coarsened exact matching identified the most comparable patient populations based on sex, age, intent, injury type, injury mechanism, Injury Severity Score, Glasgow Coma Score, and systolic blood pressure. Conditional logistic regression generated odds ratios for mortality among the entire sample and clinically relevant subgroups. Results: Coarsened exact matching matched 97.9% of the Groote Schuur Hospital patient sample, resulting in 3,206 matched‐pairs between the Groote Schuur Hospital and National Trauma Databank cohorts. Conditional logistic regression revealed an odds ratio of mortality of 1.67 (95% confidence interval, 1.10–2.52) for patients at Groote Schuur Hospital compared with matched patients from the National Trauma Databank. Subset analyses revealed significantly increased odds of mortality among patients with blunt injuries (odds ratio 3.40, 95% confidence interval, 1.68–6.88) and patients with a Glasgow Coma Score of 8 or lower (odds ratio 4.33, 95% confidence interval, 2.10–8.95). No statistically significant difference was identified among patients with penetrating injuries or with a Glasgow Coma Score >8 (P value .90 and .39, respectively). Conclusion: International comparisons of interhospital variation in risk‐adjusted outcomes following trauma can identify opportunities for quality improvement and have the potential to measure the impact of any corrective strategy implemented.

An Objective Assessment of the Surgical Trainee in an Urban Trauma Unit in South Africa: A Pilot Study

BackgroundSurgical outcomes are provider specific. This prospective audit describes the surgical activity of five general surgery residents on their trauma surgery rotation. It was hypothesized that the operating surgical trainee is an independent risk factor for adverse outcomes following major trauma.Materials and methodsThis is a prospective cohort study. All patients admitted, over a 6-month period (August 2014–January 2015), following trauma requiring a major operation performed by a surgical trainee at Groote Schuur Hospital’s trauma unit in South Africa were included. Multiple logistic regression models were built to compare risk-adjusted surgical outcomes between trainees. The primary outcome measure was major in-hospital complications.ResultsA total of 320 major operations involving 341 procedures were included. The mean age was 28.49xa0years (range 13–64), 97.2xa0% were male with a median ISS of 9 (IQR 1–41). Mechanism of injury was penetrating in 93.42xa0% of cases of which 51.86xa0% were gunshot injuries. Surgeon A consistently had the lowest risk-adjusted outcomes and was used as the reference for all outcomes in the regression models. Surgeon B, D, and E had statistically significant higher rates of major in-hospital complications than Surgeon A and C, after adjusting for multiple confounders. The final model used to calculate the risk estimates for the primary outcome had a ROC of 0.8649.ConclusionRisk-adjusted surgical outcomes vary by operating surgical trainee. The analysis thereof can add value to the objective assessment of a surgical trainee.

Data Improvement Through Simplification: Implications for Low-Resource Settings

BackgroundThe focus of many data collection efforts centers on creation of more granular data. The assumption is that more complex data are better able to predict outcomes. We hypothesized that data are often needlessly complex. We sought to demonstrate this concept by examination of the American Society of Anesthesiologists (ASA) scoring system.MethodsFirst, we created every possible consecutive two, three and four category combinations of the current five category ASA score. This resulted in 14 combinations of simplified ASA. We compared the predictive ability of these simplified scores for postoperative outcomes for 2.3 million patients in the NSQIP database. Individual model performance was assessed by comparing receiver operator characteristic (ROC) curves for each model with the standard ASA.ResultsTwo of our 4-category models and one of our 3-category models had ability to predict all outcomes equivalent to standard ASA. These results held for all outcomes and on all subgroups tested. The performance of the three best performing simplified ASA scores were also equivalent to the standard ASA score in the univariate analysis and when included in a multivariate model.ConclusionsIt is assumed that the most granular data and use of the largest number of variables for risk-adjusted predictions will increase accuracy. This complexity is often at the expense of utility. Using the single best predictor in surgical outcomes research, we have shown this is not the case.xa0In this example, we demonstrate that one can simplify ASA into a 3-category variable without losing any ability to predict outcomes.