Are the frail destined to fail? Frailty index as predictor of surgical morbidity and mortality in the elderly

Farhat, Joseph; Velanovich, Vic; Falvo, Anthony; Horst, H. Mathilda; Swartz, Andrew; Patton, Joe H.; Rubinfeld, Ilan

doi:10.1097/ta.0b013e3182542fab

Cited by 437 publications

(255 citation statements)

References 14 publications

Supporting

Mentioning

238

Contrasting

Unclassified

Order By: Relevance

“…In this study, we use the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), a high-fidelity database of perioperative information, to develop and evaluate two geriatric-specific surgical risk scores. The first is a model that closely approximates the modified frailty index (mFI), 15–17 and therefore includes fields that must be manually entered by NSQIP participant institutions (e.g., transfer status, functional level). Though these are simple parameters and do not require a geriatrics consult, they still require input from a trained individual, are subject to input error, and cost $10,000–29,000 per institution.…”

Section: Introductionmentioning

confidence: 99%

Can Routine Preoperative Data Predict Adverse Outcomes in the Elderly? Development and Validation of a Simple Risk Model Incorporating a Chart-Derived Frailty Score

Amrock

Neuman

Lin

et al. 2014

Journal of the American College of Surgeons

View full text Add to dashboard Cite

Background Frailty has emerged as an important predictor of operative risk among elderly surgical patients. However, the complexity of prospective frailty scores has limited their widespread use. Our goal was to develop two frailty-based surgical risk models employing only routine preoperative data. Our hypothesis was these models could easily integrate into an electronic medical record (EMR) to predict 30-day morbidity and mortality. Study Design ACS-NSQIP participant use files from 2005–2010 were reviewed, and patients ≥65 years old who underwent elective lower gastrointestinal surgery were identified. Two multivariate logistic regression models were constructed and internally cross-validated. The first included simple functional data, a comorbidity index based on the Charlson Comorbidity Index, demographics, BMI, and laboratory data (albumin <3.4g/dL, hematocrit<35%, creatinine>2mg/dL). The second model contained only parameters that can directly auto-populate from an EMR: demographics, laboratory data, BMI, and ASA score. To further assess diagnostic accuracy, receiver operating characteristic (ROC) curves were constructed. Results 76,106 patients met criteria for inclusion. 30-day mortality was seen in 2,853 patients or 3.7% of the study population. 18,436 patients (24.2%) experienced major complication. The c-statistic of the first expanded model was 0.813 for mortality and 0.629 for morbidity. The second simplified model had a c-statistic of 0.795 for mortality and 0.621 for morbidity. Both models were well calibrated per the Hosmer-Lemeshow test. Conclusions Our work demonstrates that routine preoperative data can approximate frailty and predict geriatric-specific surgical risk. The models’ predicative power was comparable to that of established prospective frailty scores. Our calculator could be used as a low cost simple screen for high-risk individuals who may require further evaluation or specialized services.

show abstract

Section: Introductionmentioning

confidence: 99%

Can Routine Preoperative Data Predict Adverse Outcomes in the Elderly? Development and Validation of a Simple Risk Model Incorporating a Chart-Derived Frailty Score

Amrock

Neuman

Lin

et al. 2014

Journal of the American College of Surgeons

View full text Add to dashboard Cite

show abstract

“…6,7 Frailty has been utilized to analyze multiple postoperative complications, including mortality. 8,9 However, there is insufficient literature regarding the impact of unplanned reintubation in the elderly population.…”

Section: Introductionmentioning

confidence: 99%

Association of Unplanned Reintubation with Higher Mortality in Old, Frail Patients: A National Surgical Quality-Improvement Program Analysis

Karamanos

Schmoekel

Blyden

et al. 2016

TPJ

View full text Add to dashboard Cite

Background: Unplanned postoperative reintubation increases the risk of mortality, but associated factors are unclear.Objective: To elucidate factors associated with increased mortality risk in patients with unplanned postoperative reintubation.Design: Retrospective study. Patients older than 40 years who underwent unplanned reintubation from 2005 to 2010 were identified using the American College of Surgeons National Surgical Quality Improvement Program database. Multiple regression models were used to examine the impact on mortality of factors that included the modified frailty index (mFI) we developed, American Society of Anesthesiologists (ASA) score, age decile, and days to reintubation.Main Outcome Measure: Mortality.Results: A total of 17,051 postoperative reintubations in adults were analyzed. Overall mortality was 29.4% (n = 5009). On postoperative day 1, 4434 patients were reintubated and 878 (19.8%) died. On postoperative day 7 and beyond, 6329 patients were reintubated and 2215 (35.0%) died. Increasing mFI resulted in increasing incidence of mortality (mFl of 0 = 20.5% mortality vs mFl of 0.37-0.45 = 41.7% mortality). As ASA score increased from 1 to 5, reintubation was associated with a mortality of 12.1% to 41.6%, respectively. Similarly, increasing age decile was associated with increasing incidence of mortality (40-49 years, 17.9% vs 80-89 years, 42.1%). After adjustment for confounding factors, mFI, ASA score, age decile, and increasing number of days to reintubation were independently and significantly associated with increased mortality in the study population.Conclusion: Among patients who underwent unplanned reintubation, older and more frail patients had an increased risk of mortality.

show abstract

“…The modified frailty index is an 11-point score created form the National Surgical Quality Improvement Program with the intent of helping hospitals risk adjust patients(8, 9). It considers a combination of chronic medical conditions, pre-operative functional status, and delirium to create a risk adjusted score.…”

Section: Discussionmentioning

confidence: 99%

“…For each patient record, detailed clinical variables were collected including age, sex, body mass index (BMI) at time of operation, number of comorbidities, number of medications at time of operation, , pre-operative albumin and pre-albumin taken within 48 hours before operation, pre-op parenteral nutrition (PN) as defined by any preoperative PN use before operation, history of inflammatory bowel disease (IBD) and history of malignancy, and past medical history variables making up the modified frailty index (MFI)(8, 9) The MFI score is a method derived from the Canadian Study of Health and Aging Clinical Frailty Scale. The MFI is an abbreviated version made up of eleven binominal variables; the presence of diabetes, congestive heart failure, hypertension, stroke, history of stroke with residual neurologic deficit, dependent functional status, myocardial infarction, peripheral vascular disease, chronic obstructive pulmonary disease/pneumonia, recent coronary artery bypass or percutaneous coronary intervention, and presence of delirium or altered sensorium(9). To date, this measure has been found to be a strong predictor of complications and mortality in multiple different operations(8, 9).…”

Section: Methodsmentioning

confidence: 99%

“…The MFI is an abbreviated version made up of eleven binominal variables; the presence of diabetes, congestive heart failure, hypertension, stroke, history of stroke with residual neurologic deficit, dependent functional status, myocardial infarction, peripheral vascular disease, chronic obstructive pulmonary disease/pneumonia, recent coronary artery bypass or percutaneous coronary intervention, and presence of delirium or altered sensorium(9). To date, this measure has been found to be a strong predictor of complications and mortality in multiple different operations(8, 9). …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Computed Tomography–Measured Psoas Density Predicts Outcomes After Enterocutaneous Fistula Repair

Evans

Yoo

2017

J Parenter Enteral Nutr

View full text Add to dashboard Cite

BackgroundLow muscle mass and quality are associated with poor surgical outcomes. We evaluated computed tomography (CT)–measured psoas muscle density as a marker of muscle quality and physiologic reserve and hypothesized that it predicts poor outcomes after enterocutaneous fistula repair (ECF).MethodsWe conducted a retrospective cohort study of patients 18–90 years old with ECF who failed nonoperative management, requiring elective operative repair at The Ohio State University (2005–2016), and who received preoperative abdomen/pelvis CT scan with intravenous contrast within 3 months of the operation. Psoas Hounsfield unit average calculations were measured at the L3 level. One‐year leak rate, mortality (90 days, 1 years, and 3 years), complication risk, length of stay, dependent discharge, and 30‐day readmission were compared with Hounsfield unit average calculation (HUAC).ResultsOne hundred patients met inclusion criteria. Patients were stratified into interquartile ranges based on HUAC. The lowest HUAC interquartile was our low muscle quality (LMQ) cutoff, which was associated with 1‐year leak (relative risk [RR] = 2.10, P < .005), 1‐year mortality (RR = 2.22, P < .04) and 3‐year mortality (RR = 2.13, P < .007), complication risk (RR = 1.54, P < .001), and dependent discharge (RR = 2.50, P < .004) compared to patients without LMQ.ConclusionsPsoas muscle density is a significant predictor of poor outcomes in ECF repair. This readily available measure of physiologic reserve can identify patients with ECF who have increased risk and may benefit from additional interventions and recovery time before operative repair.

show abstract

Are the frail destined to fail? Frailty index as predictor of surgical morbidity and mortality in the elderly

Abstract: Prognostic study, level II.

Cited by 437 publications

References 14 publications

Can Routine Preoperative Data Predict Adverse Outcomes in the Elderly? Development and Validation of a Simple Risk Model Incorporating a Chart-Derived Frailty Score

Can Routine Preoperative Data Predict Adverse Outcomes in the Elderly? Development and Validation of a Simple Risk Model Incorporating a Chart-Derived Frailty Score

Association of Unplanned Reintubation with Higher Mortality in Old, Frail Patients: A National Surgical Quality-Improvement Program Analysis

Computed Tomography–Measured Psoas Density Predicts Outcomes After Enterocutaneous Fistula Repair

Contact Info

Product

Resources

About