Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care.

OBJECTIVE: The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined. DESIGN: A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices. SETTING: 743 hospitals in France. PARTICIPANTS: All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days. RESULTS: The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698). CONCLUSIONS: Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.

External validation of the Hospital Frailty Risk Score in France.

BACKGROUND: The Hospital Frailty Risk Score (HFRS) has made it possible internationally to identify subgroups of patients with characteristics of frailty from routinely collected hospital data. OBJECTIVE: To externally validate the HFRS in France. DESIGN: A retrospective analysis of the French medical information database. SETTING: 743 hospitals in Metropolitan France. SUBJECTS: All patients aged 75 years or older hospitalised as an emergency in 2017 (n = 1,042,234). METHODS: The HFRS was calculated for each patient based on the index stay and hospitalisations over the preceding 2 years. Main outcome measures were 30-day in-patient mortality, length of stay (LOS) >10 days and 30-day readmissions. Mixed logistic regression models were used to investigate the association between outcomes and HFRS score. RESULTS: Patients with high HFRS risk were associated with increased risk of mortality and prolonged LOS (adjusted odds ratio [aOR] = 1.38 [1.35-1.42] and 3.27 [3.22-3.32], c-statistics = 0.676 and 0.684, respectively), while it appeared less predictive of readmissions (aOR = 1.00 [0.98-1.02], c-statistic = 0.600). Model calibration was excellent. Restricting the score to data prior to index admission reduced discrimination of HFRS substantially. CONCLUSIONS: HFRS can be used in France to determine risks of 30-day in-patient mortality and prolonged LOS, but not 30-day readmissions. Trial registration: Reference ID on clinicaltrials.gov: ID: NCT03905629.

A time-adjusted control chart for monitoring surgical outcome variations.

BACKGROUND: Statistical Process Control (SPC) tools providing feedback to surgical teams can improve patient outcomes over time. However, the quality of routinely available hospital data used to build these tools does not permit full capture of the influence of patient case-mix. We aimed to demonstrate the value of considering time-related variables in addition to patient case-mix for detection of special cause variations when monitoring surgical outcomes with control charts. METHODS: A retrospective analysis from the French nationwide hospital database of 151,588 patients aged 18 and older admitted for colorectal surgery between January 1st, 2014, and December 31st, 2018. GEE multilevel logistic regression models were fitted from the training dataset to predict surgical outcomes (in-patient mortality, intensive care stay and reoperation within 30-day of procedure) and applied on the testing dataset to build control charts. Surgical outcomes were adjusted on patient case-mix only for the classical chart, and additionally on secular (yearly) and seasonal (quarterly) trends for the enhanced control chart. The detection of special cause variations was compared between those charts using the Cohen's Kappa agreement statistic, as well as sensitivity and positive predictive value with the enhanced chart as the reference. RESULTS: Within the 5-years monitoring period, 18.9% (28/148) of hospitals detected at least one special cause variation using the classical chart and 19.6% (29/148) using the enhanced chart. 59 special cause variations were detected overall, among which 19 (32.2%) discordances were observed between classical and enhanced charts. The observed Kappa agreement between those charts was 0.89 (95% Confidence Interval [95% CI], 0.78 to 1.00) for detecting mortality variations, 0.83 (95% CI, 0.70 to 0.96) for intensive care stay and 0.67 (95% CI, 0.46 to 0.87) for reoperation. Depending on surgical outcomes, the sensitivity of classical versus enhanced charts in detecting special causes variations ranged from 0.75 to 0.89 and the positive predictive value from 0.60 to 0.89. CONCLUSION: Seasonal and secular trends can be controlled as potential confounders to improve signal detection in surgical outcomes monitoring over time.

Control Charts Usage for Monitoring Performance in Surgery: A Systematic Review.

OBJECTIVES: The control chart is a graphical tool for data interpretation that detects aberrant variations in specific metrics, ideally leading to the identification of special causes that can be resolved. A clear assessment of control chart utilization and its potential impact in surgery is required to justify recommendations for its dissemination. This review aims to describe how performance monitoring using control charts was used over time in surgery. METHODS: A systematic search of PubMed regarding statistical process control in surgery from its inception until December 2019 was performed using Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Information extracted from selected publications included study aim and population setting, monitored indicators, control charts methodological parameters, and implementation strategy. RESULTS: One hundred thirteen studies met the selection criteria with a median of 1916 monitored patients. Overall, 57.5% of studies focused on control chart methodology, 24.8% aimed at evaluating performance changes using control charts retrospectively, and 17.7% implemented control charts for continuous quality improvement prospectively. Although there was a great diversity of used indicators and charting tools, the evaluation of patient safety (72.6%) or efficiency (15.9%) metrics based on Shewhart control chart (33.6%) or cumulative sum chart (54.9%) were common. To foster control charts implementation, 14 studies promoted their periodic review, but only three assessed their impact on patient outcomes. CONCLUSIONS: The scientific literature supports the feasibility and utility of control chart to improve patient safety in multiple surgical settings. Additional studies are necessary to reveal the optimal manner in which to implement this affordable tool in surgical practice.

Excess mortality among non-COVID-19 surgical patients attributable to the exposure of French intensive and intermediate care units to the pandemic.

PURPOSE: The mobilization of most available hospital resources to manage coronavirus disease 2019 (COVID-19) may have affected the safety of care for non-COVID-19 surgical patients due to restricted access to intensive or intermediate care units (ICU/IMCUs). We estimated excess surgical mortality potentially attributable to ICU/IMCUs overwhelmed by COVID-19, and any hospital learning effects between two successive pandemic waves. METHODS: This nationwide observational study included all patients without COVID-19 who underwent surgery in France from 01/01/2019 to 31/12/2020. We determined pandemic exposure of each operated patient based on the daily proportion of COVID-19 patients among all patients treated within the ICU/IMCU beds of the same hospital during his/her stay. Multilevel models, with an embedded triple-difference analysis, estimated standardized in-hospital mortality and compared mortality between years, pandemic exposure groups, and semesters, distinguishing deaths inside or outside the ICU/IMCUs. RESULTS: Of 1,870,515 non-COVID-19 patients admitted for surgery in 655 hospitals, 2% died. Compared to 2019, standardized mortality increased by 1% (95% CI 0.6-1.4%) and 0.4% (0-1%) during the first and second semesters of 2020, among patients operated in hospitals highly exposed to pandemic. Compared to the low-or-no exposure group, this corresponded to a higher risk of death during the first semester (adjusted ratio of odds-ratios 1.56, 95% CI 1.34-1.81) both inside (1.27, 1.02-1.58) and outside the ICU/IMCU (1.98, 1.57-2.5), with a significant learning effect during the second semester compared to the first (0.76, 0.58-0.99). CONCLUSION: Significant excess mortality essentially occurred outside of the ICU/IMCU, suggesting that access of surgical patients to critical care was limited.