Marco D Huesch1, Elizabeth Currid-Halkett2, Jason N Doctor3. 1. Sol Price School of Public Policy, University of Southern California, Los Angeles, CA; Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA; Department of Community and Family Medicine, School of Medicine, and Health Sector Management Area, Fuqua School of Business, Duke University, Durham, NC. Electronic address: huesch@usc.edu. 2. Sol Price School of Public Policy, University of Southern California, Los Angeles, CA. 3. Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA; School of Pharmacy, University of Southern California, Los Angeles, CA.
Abstract
OBJECTIVE: Prelabor cesareans in women without a prior cesarean is an important quality measure, yet one that is seldom tracked. We estimated patient-level risks and calculated how sensitive hospital rankings on this proposed quality metric were to risk adjustment. STUDY DESIGN: This retrospective cohort study linked Californian patient data from the Agency for Healthcare Research and Quality with hospital-level operational and financial data. Using the outcome of primary prelabor cesarean, we estimated patient-level logistic regressions in progressively more detailed models. We assessed incremental fit and discrimination, and aggregated the predicted patient-level event probabilities to construct hospital-level rankings. RESULTS: Of 408,355 deliveries by women without prior cesareans at 254 hospitals, 11.0% were prelabor cesareans. Including age, ethnicity, race, insurance, weekend and unscheduled admission, and 12 well-known patient risk factors yielded a model c-statistic of 0.83. Further maternal comorbidities, and hospital and obstetric unit characteristics only marginally improved fit. Risk adjusting hospital rankings led to a median absolute change in rank of 44 places compared to rankings based on observed rates. Of the 48 (49) hospitals identified as in the best (worst) quintile on observed rates, only 23 (18) were so identified by the risk-adjusted model. CONCLUSION: Models predict primary prelabor cesareans with good discrimination. Systematic hospital-level variation in patient risk factors requires risk adjustment to avoid considerably different classification of hospitals by outcome performance. An opportunity exists to define this metric and report such risk-adjusted outcomes to stakeholders.
OBJECTIVE: Prelabor cesareans in women without a prior cesarean is an important quality measure, yet one that is seldom tracked. We estimated patient-level risks and calculated how sensitive hospital rankings on this proposed quality metric were to risk adjustment. STUDY DESIGN: This retrospective cohort study linked Californian patient data from the Agency for Healthcare Research and Quality with hospital-level operational and financial data. Using the outcome of primary prelabor cesarean, we estimated patient-level logistic regressions in progressively more detailed models. We assessed incremental fit and discrimination, and aggregated the predicted patient-level event probabilities to construct hospital-level rankings. RESULTS: Of 408,355 deliveries by women without prior cesareans at 254 hospitals, 11.0% were prelabor cesareans. Including age, ethnicity, race, insurance, weekend and unscheduled admission, and 12 well-known patient risk factors yielded a model c-statistic of 0.83. Further maternal comorbidities, and hospital and obstetric unit characteristics only marginally improved fit. Risk adjusting hospital rankings led to a median absolute change in rank of 44 places compared to rankings based on observed rates. Of the 48 (49) hospitals identified as in the best (worst) quintile on observed rates, only 23 (18) were so identified by the risk-adjusted model. CONCLUSION: Models predict primary prelabor cesareans with good discrimination. Systematic hospital-level variation in patient risk factors requires risk adjustment to avoid considerably different classification of hospitals by outcome performance. An opportunity exists to define this metric and report such risk-adjusted outcomes to stakeholders.
Authors: Ilir Hoxha; Lamprini Syrogiannouli; Xhyljeta Luta; Kali Tal; David C Goodman; Bruno R da Costa; Peter Jüni Journal: BMJ Open Date: 2017-02-17 Impact factor: 2.692
Authors: Ilir Hoxha; Lamprini Syrogiannouli; Medina Braha; David C Goodman; Bruno R da Costa; Peter Jüni Journal: BMJ Open Date: 2017-08-21 Impact factor: 2.692
Authors: Maarten D H Vink; Piet J G M de Bekker; Xander Koolman; Maurits W van Tulder; Ralph de Vries; Ben Willem J Mol; Eric J E van der Hijden Journal: BMC Pregnancy Childbirth Date: 2020-08-20 Impact factor: 3.007