Shahid Shafi M.D.

Zogg, C. K., O. A. Olufajo, W. Jiang, A. Bystricky, J. W. Scott, S. Shafi, J. M. Havens, A. Salim, A. J. Schoenfeld and A. H. Haider (2017). “The need to consider longer-term outcomes of care: Racial/ethnic disparities among adult and older adult emergency general surgery patients at 30, 90, and 180 days.” Ann Surg 266(1): 66-75.

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OBJECTIVES: Following calls from the National Institutes of Health and American College of Surgeons for “urgently needed” research, the objectives of the present study were to (1) ascertain whether differences in 30/90/180-day mortality, major morbidity, and unplanned readmissions exist among adult (18-64 yr) and older adult (>/=65 yr) emergency general surgery (EGS) patients; (2) vary by diagnostic category; and (3) are explained by variations in insurance, income, teaching status, hospital EGS volume, and a hospital’s proportion of minority patients. BACKGROUND: Racial/ethnic disparities have been described in in-hospital and 30-day settings. How longer-term outcomes compare-a critical consideration for the lived experience of patients-has, however, only been limitedly considered. METHODS: Survival analysis of 2007 to 2011 California State Inpatient Database using Cox proportional hazards models. RESULTS: A total of 737,092 adults and 552,845 older adults were included. In both cohorts, significant differences in 30/90/180-day mortality, major morbidity, and unplanned readmissions were found, pointing to persistently worse outcomes between non-Hispanic Black and White patients [180-d readmission hazard ratio (95% confidence interval):1.04 (1.03-1.06)] and paradoxically better outcomes among Hispanic adults [0.85 (0.84-0.86)] that were not encountered among Hispanic older adults [1.06 (1.04-1.07)]. Stratified results demonstrated robust morbidity and readmission trends between non-Hispanic Black and White patients for the majority of diagnostic categories, whereas variations in insurance/income/teaching status/EGS volume/proportion of minority patients all significantly altered the effect-combined accounting for up to 80% of risk-adjusted differences between racial/ethnic groups. CONCLUSIONS: Racial/ethnic disparities exist in longer-term outcomes of EGS patients and are, in part, determined by differences in factors associated with emergency care. Efforts such as these are needed to understand the interplay of influences-both in-hospital and during the equally critical, postacute phase-that underlie disparities’ occurrence among surgical patients.

Callender, L., R. Brown, S. Driver, M. Dahdah, A. Collinsworth and S. Shafi (2017). “Process for developing rehabilitation practice recommendations for individuals with traumatic brain injury.” BMC Neurol 17(1): 54.

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BACKGROUND: Attempts at measuring quality of rehabilitation care are hampered by a gap in knowledge translation of evidence-based approaches and lack of consensus on best practices. However, adoption of evidence-based best practices is needed to minimize variations and improve quality of care. Therefore, the objective of this project was to describe a process for assessing the quality of evidence of clinical practices in traumatic brain injury (TBI) rehabilitative care. METHODS: A multidisciplinary team of clinicians developed discipline-specific clinical questions using the Population, Intervention, Control, Outcome process. A systematic review of the literature was conducted for each question using Pubmed, CINAHL, PsychInfo, and Allied Health Evidence databases. Team members assessed the quality, level, and applicability of evidence utilizing a modified Oxford scale, the Agency for Healthcare Research and Quality Methods Guide, and a modified version of the Grading of Recommendations, Assessment, Development, and Evaluation scale. RESULTS: Draft recommendations for best-practice were formulated and shared with a Delphi panel of clinical representatives and stakeholders to obtain consensus. CONCLUSION: Evidence-based practice guidelines are essential to improve the quality of TBI rehabilitation care. By using a modified quality of evidence assessment tool, we established a process to gain consensus on practice recommendations for individuals with TBI undergoing rehabilitation.

Ogola, G. O., A. Haider and S. Shafi (2017). “Hospitals with higher volumes of emergency general surgery patients achieve lower mortality rates: A case for establishing designated centers for emergency general surgery.” J Trauma Acute Care Surg 82(3): 497-504.

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BACKGROUND: Higher volume has been associated with lower mortality for several surgical diseases. It is not known if this relationship exists in the management of Emergency General Surgery (EGS). Our hypothesis was that EGS patients treated at hospitals with higher EGS volume experienced lower mortality rates than those treated at low-volume hospitals. METHODS: This was a retrospective analysis of 2010 National Inpatient Sample data, maintained by the Agency for Healthcare Quality and Research as a representative national sample of inpatients. Patients with EGS diseases were identified using American Association for the Surgery of Trauma definitions using ICD9 codes (2,640,725 patients from 943 hospitals). Multivariable hierarchical logistic regression model was used to estimate the risk-standardized mortality rate (RSMR) for each hospital, adjusted for patient (age, sex, race, ethnicity, insurance type, socioeconomic status, comorbidities) and hospital (region, location, bed size, teaching status, ownership) characteristics. A cubic spline regression model with 4 knots was used to identify the volume associated with low mortality rates. CONCLUSION: EGS patients treated at hospitals with a higher volume of EGS patients experienced lower mortality rates, with a possible threshold of 688 patients per year. A regionalized system of EGS care where complex patients are treated at large-volume centers may improve patient outcomes.

Ogola, G. O., A. Haider and S. Shafi (2016). “Hospitals with higher volumes of emergency general surgery patients achieve lower mortality rates: A case for establishing designated centers for emergency general surgery.” J Trauma Acute Care Surg: 2016 Dec [Epub ahead of print].

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BACKGROUND: Higher volume has been associated with lower mortality for several surgical diseases. It is not known if this relationship exists in the management of Emergency General Surgery (EGS). Our hypothesis was that EGS patients treated at hospitals with higher EGS volume experienced lower mortality rates than those treated at low-volume hospitals. METHODS: This was a retrospective analysis of 2010 National Inpatient Sample data, maintained by the Agency for Healthcare Quality and Research as a representative national sample of inpatients. Patients with EGS diseases were identified using American Association for the Surgery of Trauma definitions using ICD-9 codes (2,640,725 patients from 943 hospitals). Multivariable hierarchical logistic regression model was used to estimate risk-standardized mortality rates (RSMR) for each hospital, adjusted for patient (age, sex, race, ethnicity, insurance type, socioeconomic status, comorbidities) and hospital characteristics (region, location, bed size, teaching status, and ownership). A cubic spline regression model with 4-knots was used to identify the volume associated with low mortality rates. RESULTS: The volume of EGS patients treated was inversely associated with hospital mortality rate. RSMR in hospitals in the highest quintile of volume (median, 7424 patients) was 1.62% (95% CI: 1.61-1.64%); at hospitals in the lowest quintile of volume (median, 68 patients), it was 6.1% (95% CI: 6.0-6.2%) (p <0.0001). Mortality rate stabilized at an annual volume of 688 (95% CI: 554-753) patients. The mortality rate in hospitals that treated fewer than 688 patients was 5.0% (95% CI: 4.8-5.1%), compared to 1.99% (95% CI: 1.96-2.01%) at those that treated 688 or more patients (p<0.0001). CONCLUSION: EGS patients treated at hospitals with a higher volume of EGS patients experienced lower mortality rates, with a possible threshold of 688 patients per year. A regionalized system of EGS care where complex patients are treated at large volume centers may improve patient outcomes.

Dahdah, M. N., S. Barnes, A. Buros, R. Dubiel, C. Dunklin, L. Callender, C. Harper, A. Wilson, R. Diaz-Arrastia, T. Bergquist, M. Sherer, G. Whiteneck, C. Pretz, R. D. Vanderploeg and S. Shafi (2016). “Variations in inpatient rehabilitation functional outcomes across centers in the traumatic brain injury model systems study and the influence of demographics and injury severity on patient outcomes.” Arch Phys Med Rehabil 97(11): 1821-1831.

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OBJECTIVE: To compare patient functional outcomes across Traumatic Brain Injury Model Systems (TBIMS) rehabilitation centers using an enhanced statistical model and to determine factors that influence those outcomes. DESIGN: Multicenter observational cohort study. SETTING: TBIMS centers. PARTICIPANTS: Patients with traumatic brain injury (TBI) admitted to 19 TBIMS rehabilitation centers from 2003-2012 (N=5505). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Functional outcomes of patients with TBI. RESULTS: Individuals with lower functional status at the time of admission, longer duration of posttraumatic amnesia, and higher burden of medical comorbidities continued to have worse functional outcomes at discharge from inpatient rehabilitation and at the 1-year follow-up, whereas those who were employed at the time of injury had better outcomes at both time periods. Risk-adjusted patient functional outcomes for patients in most TBIMS centers were consistent with previous research. However, there were wide performance differences for a few centers even after using more recently collected data, improving on the regression models by adding predictors known to influence functional outcomes, and using bootstrapping to eliminate confounds. CONCLUSIONS: Specific patient, injury, and clinical factors are associated with differences in functional outcomes within and across TBIMS rehabilitation centers. However, these factors did not explain all the variance in patient outcomes, suggesting a role of some other predictors that remain unknown.