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Mar 19, 2025; e14607.

Risk Adjusted Continuous Monitoring of Postoperative Mortality After Cardiac Surgery.

Zahra Mobini, Ammer Saati, Turgay Ayer, Xiangqin Cui, Robert Krafty, Alex H S Harris, Nader N Massarweh
Author Information
  1. Zahra Mobini: Scheller College of Business, Georgia Tech, Atlanta, Georgia, USA.
  2. Ammer Saati: Atlanta VA Health Care System, Research Service Line, Decatur, Georgia, USA.
  3. Turgay Ayer: H. Milton Stuart School of Industrial and Systems Engineering, Georgia Tech, Atlanta, Georgia, USA.
  4. Xiangqin Cui: Atlanta VA Health Care System, Research Service Line, Decatur, Georgia, USA.
  5. Robert Krafty: Emory University Rollins School of Public Health, Department of Biostatistics, Atlanta, Georgia, USA.
  6. Alex H S Harris: Veterans Affairs Health Services Research and Development Center for Innovation to Implementation, Palo Alto Veterans Affairs Health Care System, Palo Alto, California, USA.
  7. Nader N Massarweh: Atlanta VA Health Care System, Surgical and Perioperative Care, Decatur, Georgia, USA. ORCID
PMID: 40105015 DOI: 10.1111/1475-6773.14607

Abstract

OBJECTIVE: To compare continuous monitoring with a risk-adjusted cumulative sum (CUSUM) to standard episodic risk-adjusted evaluation for the detection of hospitals with higher-than-expected postoperative mortality after cardiac surgery.
STUDY SETTING AND DESIGN: In this national, observational, hospital-level study, the number of hospitals identified with higher-than-expected quarterly, risk-adjusted 30-day mortality and time to identification were compared using standard episodic evaluation (i.e., observed-to-expected [O-E] ratios) and the risk-adjusted CUSUM.
DATA SOURCES AND ANALYTIC SAMPLE: VA Surgical Quality Improvement Program (VASQIP) data (2016-2020) for patients 18���years and older who underwent a cardiac operation at a Veterans Affairs (VA) hospital.
PRINCIPAL FINDINGS: The cohort included 20,927 patients treated at 41 hospitals across 20 quarters of data. Overall, 1.8% of hospital quarters were identified using O-E compared to 3.8% with CUSUM. Hospitals concurrently identified using both CUSUM and O-E were identified a median of 17���days earlier with CUSUM (interquartile range [IQR] 7-51���days before quarter end). This translated to a median of 12 (IQR 8-37) surgical cases and 71 (IQR 34-331) postoperative inpatient days occurring after a CUSUM signal but before the quarter ended. At hospitals identified by CUSUM but not O-E, a median of 2 deaths (IQR 2-2) during a median of 22���days (IQR 12-38) triggered detection.
CONCLUSIONS: CUSUM identifies hospitals with higher-than-expected mortality rates earlier than episodic analysis. Considering the time lag between data collection and report generation by national quality improvement (QI) programs, CUSUM represents a potentially useful tool that could facilitate more real-time recognition of performance concerns and encourage earlier implementation of interventions that can help avoid potentially preventable patient harm. Balancing sensitivity with the risk of false signaling will be essential for ensuring its effective application in national QI efforts.

Keywords

cardiac surgery mortality outcomes quality improvement

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Grants

  1. R01 HL157323/NHLBI NIH HHS
  2. R01 HL157323/NIH HHS
Journal Article

Word Cloud

Created with Highcharts 10.0.0CUSUMhospitalsidentifiedrisk-adjustedmortalitymedianIQRepisodichigher-than-expectedcardiacnationalusingdataO-EearlierstandardevaluationdetectionpostoperativesurgeryANDtimecomparedVApatientshospital20quarters8%quarterqualityimprovementQIpotentiallyOBJECTIVE:comparecontinuousmonitoringcumulativesumSTUDYSETTINGDESIGN:observationalhospital-levelstudynumberquarterly30-dayidentificationieobserved-to-expected[O-E]ratiosDATASOURCESANALYTICSAMPLE:SurgicalQualityImprovementProgramVASQIP2016-202018���yearsolderunderwentoperationVeteransAffairsPRINCIPALFINDINGS:cohortincluded927treated41acrossOverall13Hospitalsconcurrently17���daysinterquartilerange[IQR]7-51���daysendtranslated128-37surgicalcases7134-331inpatientdaysoccurringsignalended2deaths2-222���days12-38triggeredCONCLUSIONS:identifiesratesanalysisConsideringlagcollectionreportgenerationprogramsrepresentsusefultoolfacilitatereal-timerecognitionperformanceconcernsencourageimplementationinterventionscanhelpavoidpreventablepatientharmBalancingsensitivityriskfalsesignalingwillessentialensuringeffectiveapplicationeffortsRiskAdjustedContinuousMonitoringPostoperativeMortalityCardiacSurgeryoutcomes

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