The influence of decreasing variable collection burden on hospital-level risk-adjustment

Andrew Hu; Marie Iwaniuk; Vanessa Thompson; Catherine Grant; Alaina Matthews; Claudia Byrd; Jacqueline Saito; Bruce Hall; Mehul V Raval

doi:10.1016/j.jpedsurg.2021.10.007

The influence of decreasing variable collection burden on hospital-level risk-adjustment

J Pediatr Surg. 2022 Sep;57(9):9-16. doi: 10.1016/j.jpedsurg.2021.10.007. Epub 2021 Oct 16.

Authors

Andrew Hu¹, Marie Iwaniuk², Vanessa Thompson², Catherine Grant², Alaina Matthews², Claudia Byrd², Jacqueline Saito³, Bruce Hall⁴, Mehul V Raval⁵

Affiliations

¹ Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, 633 N. Saint Clair St, 20th Floor, Chicago, IL 60011, USA. Electronic address: andrew.hu1@northwestern.edu.
² Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL, USA.
³ Division of Pediatric Surgery, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.
⁴ Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL, USA; Department of Surgery, Washington University School of Medicine, and BJC Healthcare, St. Louis, MO, USA.
⁵ Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, 633 N. Saint Clair St, 20th Floor, Chicago, IL 60011, USA.

PMID: 34801250
DOI: 10.1016/j.jpedsurg.2021.10.007

Abstract

Background: Risk-adjustment is a key feature of the American College of Surgeons National Surgical Quality Improvement Program-Pediatric (NSQIP-Ped). Risk-adjusted model variables require meticulous collection and periodic assessment. This study presents a method for eliminating superfluous variables using the congenital malformation (CM) predictor variable as an example.

Methods: This retrospective cohort study used NSQIP-Ped data from January 1st to December 31st, 2019 from 141 hospitals to compare six risk-adjusted mortality and morbidity outcome models with and without CM as a predictor. Model performance was compared using C-index and Hosmer-Lemeshow (HL) statistics. Hospital-level performance was assessed by comparing changes in outlier statuses, adjusted quartile ranks, and overall hospital performance statuses between models with and without CM inclusion. Lastly, Pearson correlation analysis was performed on log-transformed ORs between models.

Results: Model performance was similar with removal of CM as a predictor. The difference between C-index statistics was minimal (≤ 0.002). Graphical representations of model HL-statistics with and without CM showed considerable overlap and only one model attained significance, indicating minimally decreased performance (P = 0.058 with CM; P = 0.044 without CM). Regarding hospital-level performance, minimal changes in the number and list of hospitals assigned to each outlier status, adjusted quartile rank, and overall hospital performance status were observed when CM was removed. Strong correlation between log-transformed ORs was observed (r ≥ 0.993).

Conclusions: Removal of CM from NSQIP-Ped has minimal effect on risk-adjusted outcome modelling. Similar efforts may help balance optimal data collection burdens without sacrificing highly valued risk-adjustment in the future.

Level of evidence: Level II prognosis study.

Keywords: Congenital malformation; NSQIP pediatric; Outcomes; Pediatric surgery; Quality improvement; Risk-adjustment; Variable elimination.

MeSH terms

Child
Hospitals
Humans
Postoperative Complications* / epidemiology
Postoperative Complications* / etiology
Quality Improvement
Retrospective Studies
Risk Adjustment*
United States / epidemiology