Abstract
Retrospective impact evaluation of frailty as measured by the Memorial Sloan Kettering Frailty Index (MSK-FI) on outcomes in older women surgically treated for advanced epithelial ovarian cancer (EOC). Women ≥ 60 years with stage IIIC/IV EOC who underwent primary debulking surgery (PDS) or interval debulking surgery (IDS) were included. Medical records were reviewed for patients’ characteristics and outcomes. We retrospectively applied the MSK-FI which included 10 comorbidities and functional assessment that were extracted from medical records. The MSK-FI ranges from 0 to 11; a score of ≥ 3 was considered frail. Associations were assessed using logistic regression and Cox proportional hazards regression. We identified 79 patients treated with PDS (n = 36, 45.5%) or IDS (n = 43, 54.4%) with complete data. The prevalence of frailty based on MSK-FI was 25%. Almost half of the frail patients (47.3%) were admitted to the ICU compared to 16% of non-frail patients (p = 0.006). In univariable analysis, the MSK-FI was associated with postoperative complications [OR 1.57 (95% CI 1.04–2.37), p = 0.03] and ICU admission [OR 2.05 (95% CI 1.30–3.23), p = 0.002], but not with readmission rate [OR 1.29 (95% CI 0.65–2.59), p = 0.5], postoperative mortality [OR 1.02 (95% CI 0.51–2.00), p = 0.9], and hospital stay [β 0.60 (95% CI − 1.19–2.41)]. In multivariable analysis, the frailty index was independently associated with postoperative complications [OR 1.54 (95% CI 1.02–2.34), p = 0.04] and ICU admissions [OR 1.97 (95% CI 1.23–3.16), p = 0.004]. Frailty, based on the Memorial Sloan Kettering Frailty Index, is associated with adverse postoperative outcomes in older women with advanced ovarian cancer, suggesting that MSK-FI can improve the predictive ability of current surgical assessment tools.
Keywords: Frailty, Memorial Sloan Kettering Frailty Index, Advanced ovarian cancer, Gynecological surgery, Preoperative assessment
Introduction
The geriatric population has increased considerably worldwide in the last decades. In the USA, there were more than 43 million people older than 65 years by 2012, with projections predicting an increase in 60% of this estimate by 2060 [1, 2].
The term frailty started to be used by doctors to refer to an older adult that appears unstable and vulnerable to certain diseases and is considered a practical concept in the care of the geriatric population [3]. Frailty can therefore conduce to a higher incidence of adverse effects and decreased quality of life and independence after relatively small physiologic insults such as surgical intervention [4, 5].
In order to be able to study frailty, several scores have been proposed, demonstrating utility in clinical practice. In one study, Aguilar-Frasco et al. [6] prospectively analyzed a modification of the frailty index by Searle et al. [7], which included 50 variables in the context of major abdominal surgery in older adults (> 65 years) included oncological surgeries. This index demonstrated to be a predictor for prolonged hospital stay (p ≤ 0.0001), ICU admission rates (p = 0.004), and higher mortality rates (p = 0.02) and independent predictor for postoperative complications (OR 6.38, 95% CI [2.45–16.58], p < 0.001), with a sensitivity (Se) of 70% and specificity (Sp) 67% with area under the curve (AUC) of 0.75 for perioperative complications.
However, there are few studies conducted on older patients with cancer. Surgeons treating this population should take into account factors other than age than predict the likelihood of poor postoperative outcomes. Recently, Shahrokni et al. [8] developed a new frailty index (the Memorial Sloan Kettering Frailty Index [MSK-FI]) that includes preoperative functional status based on 4 basic patient-reported activities of daily living (i.e., bathing, dressing, grooming, and walking outside the home) and 1 instrumental activity of daily living (i.e., preparing meals). Limitations in any of these activities are considered “impaired functional activity” and are given a value of 1; independent patients received a value of 0. The MSK-FI also included 10 comorbidities which are (1) chronic obstructive pulmonary disease or pneumonia within 30 days before surgery, (2) diabetes, (3) congestive heart failure, (4) myocardial infarction, (5) coronary artery disease, (6) hypertension, (7) peripheral vascular disease, (8) impaired sensorium (which incorporates Alzheimer disease, delirium, dementia, Lewy body disease, mild cognitive impairment, and memory loss), (9) cerebrovascular accident, and (10) transient ischemic attack. The score for comorbid conditions ranges from 0 to 10. Therefore, the MSK-FI final score ranging from 0 to 11 and a score ≥ 3 was considered a frail patient. They found that frail patients were likely to experience longer LOS (0.58 day; 95% CI, 0.22–0.95; p = 0.002), increased frequency of postoperative ICU admissions (OR 1.28; 95% CI, 1.04–1.58; p = 0.02), and decreased overall survival (p = 0.005).
Therefore, the MSK-FI provides the advantage of not requiring an extensive physical assessment. It can be applied effectively in a timely manner, it can be obtained indirectly from the patient’s proxy, and was shown to be associated with previously validated GA [8].
The objective of our study was to determine whether the MSK-FI is associated with adverse postoperative outcomes in older women subjected to PDS or IDS.
Methods
We performed a retrospective analysis of women ≥ 60 years with newly diagnosed advanced-stage ovarian cancer surgically treated between May 2010 and May 2020 in a single third-level academic center. Each patient’s medical record was reviewed for patients’ characteristics and outcomes. Patients that undergoing palliative or diagnostic surgeries only and patients with incomplete information were excluded from the study.
This study underwent revision and approval by the ethics committee of our institution. The authors of this manuscript declare to have treated patient data in a secure and confidential manner, following the protocols of our center.
The variables age, BMI, comorbidities, albumin, and hematocrit levels were included, as well as perioperative variables such as estimated blood loss, operative time, intraoperative complications, intraoperative or postoperative information on the residual disease, hospital stay, readmissions at 30 days, reoperations, admission to critical care unit, and mortality within 30 days. Postoperative complications were captured for the first 30 days after surgery and graded according to American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) definitions; we categorized overall in-hospital complications into minor and major complications. Overall survival was calculated from date of diagnosis to the date of last follow-up or death.
Surgical complexity was graded using a surgical complexity scoring system based upon the complexity and the number of surgical procedures performed [9], for example, hysterectomy + salpingo-oophorectomy giving, 1 point; omentectomy, 1 point; paraaortic lymphadenectomy, 1 point; pelvic lymphadenectomy, 1 point; small bowel resection, 1 point; pelvic peritoneum stripping, 1 point; abdominal peritoneum stripping, 1 point; large bowel resection, 2 points; splenectomy, 2 points; diaphragm stripping/resection, 2 points; liver resection, 2 points; and rectosigmoidectomy with anastomosis, 3 points. This gives 3 surgical complexity score (SCS) groups: (a) low: ≤ 3 points; (b) intermediate: 4 to 7 points; and (c) high: ≥ 8 points.
We retrospectively applied the MSK-FI which included 10 comorbidities and functional assessment that were extracted from medical records and we categorized patients into two groups based on their frailty index: frail and non-frail. Frail was defined as a MSK-FI ≥ 3, while non-frail was defined as a MSK-FI < 3. We established a cutoff point of 3 for dichotomizing based on previously published studies [8]. Our primary objective was to study the capacity of the index to identify postoperative complications, length of say, and mortality at 30 days. Our secondary outcome measures were readmissions, admission to ICU, overall mortality, and other variables related to poor postoperative outcomes.
Data Presentation and Statistical Analysis
All results were analyzed using IBM SPSS Statistics (version 21.0, IBM, Armonk, NY, USA). Comparative analyses of quantitative data were performed using the chi-squared test for categorical variables and Student’s t-test for continuous variables. All tests of significance were two-tailed and p values of < 0.05 indicated statistical significance.
We compared outcomes in both groups. Associations were assessed using logistic regression and Cox proportional hazards regression. Categorical variables were presented as frequencies and percentages while quantitative variables were presented as median and interquartile range (IQR).
Results
A total of 90 women with stage IIIC/IV EOC were eligible for the study. Of those, 79 were included in the total analysis; 11 were excluded due to lack of frailty data available. The median age was 64 (IQR 62–70) years, and the median BMI was 25.4 (IQR 22.5–28) kg/m2. Thirty-six patients (45%) underwent primary debulking surgery (PDS) and forty-three patients (54%) received neoadjuvant and interval debulking surgery (IDS).
MSK-FI score distribution was “0” in 14 patients (17%); “1” in 21 patients (26%); “2” in 25 patients (31%); and “3 + ” in 19 patients (24%) who qualified as frail (MSK-FI ≥ 3) according to our cutoff value. Frail patients were less likely to have undergone PDS [32 (53%) vs 4 (21%), p = 0.01] and had a higher Charlson comorbidity index (CCI) [6 (5–8) vs 5 (4–6.5), p = 0.005]. Additionally, frail patients were more likely to have low complexity surgery performed (43.0% vs 57.8%, p = 0.3) and demonstrated significantly more suboptimal surgical resection (26% vs 13%, p ≤ 0.001). However, when comparing frail and non-frail patients, median age [68 (63–73) vs 63 (62–68.5) years, p = 0.08], BMI [26.7 (23.4–29.7) vs 25.1 (22.4–27.1) kg/m2, p = 0.2], and albumin [4.1 (3.7–4.3) vs 4.1 (3.8–4.4) g/dl, p = 0.8] were not different between these groups. The rest of the demographic and clinical variables commuted are shown in Table 1.
Table 1.
Results from clinical and demographic parameters
| Characteristic | All patients (n = 79) |
Non-frail (n = 60) |
Frail (n = 19) |
p |
|---|---|---|---|---|
| Age (years), median (IQR) | 64 (62–70) | 63 (62–68.5) | 68 (63–73) | 0.08 |
| BMI (kg/m2), median (IQR) | 25.4 (22.5–28) | 25.1 (22.4–27.1) | 26.7 (23.4–29.7) | 0.2 |
| Hematocrit (%), median (IQR) | 37.5 (34–40.3) | 37.7 (34.5–40.6) | 36.6 (31.2–38) | 0.2 |
| Preoperative albumin (mg/dl), median (IQR) | 4.1 (3.1–4.3) | 4.1 (3.8–4.4) | 4.1 (3.7–4.3) | 0.8 |
| CCI, median (IQR) | 5 (5–7) | 5 (4–6.5) | 6 (5–8) | 0.005 |
| PDS, N (%) | 36 (45) | 32 (53) | 4 (21) | 0.01 |
| Neoadjuvant therapy, N (%) | 43 (54) | 28 (47) | 15 (78) | 0.01 |
| FIGO stage, N (%) | 1.0 | |||
| IIIC | 75 (94.9) | 57 (95) | 18 (94.7) | |
| IV | 4 (5.0) | 3 (5) | 1 (5.2) | |
| Surgical complexity, N (%) | 0.3 | |||
| Low | 37 (46.8) | 26 (43.3) | 11 (57.8) | |
| Intermediate | 39 (49.3) | 31 (51.6) | 8 (42.1) | |
| High | 3 (3.7) | 3 (5) | – | |
| Residual disease, N (%) | < 0.001 | |||
| Microscopic | 34 (43) | 29 (49) | 6 (31) | |
| Measurable (≤ 1 cm) | 32 (40) | 23 (38) | 8 (42) | |
| Suboptimal (> 1 cm) | 13 (16) | 8 (13) | 5 (26) |
Abbreviations: IQR, interquartile range; BMI, body mass index; CCI, Charlson comorbidity index; PDS, primary debulking surgery
Regarding the perioperative outcomes, frail patients had a higher rate of ICU admission compared with non-frail patients (47% vs 17%, p = 0.006). This is despite having a higher likelihood of low complexity surgery. Among our cohort, the frequency of the outcomes of interest were not different between frail and non-frail patients, including postoperative complications [6 (35%) vs 11 (19%), p = 0.1], readmission rate within 30 days [1 (5%) vs 3 (5%), p = 0.9], postoperative mortality within 30 days [2 (10%) vs 3 (5%), p = 0.4], and hospital stay [6 (4–8) vs 7 (4–9.5) days, p = 0.9] (Table 2). Median overall survival was 84 months (IQR 18–N/A), and it was not different between frail (30 months, IQR 11–120) and non-frail (84 months, IQR 32–N/A) patients (p = 0.1).
Table 2.
Association of Memorial Sloan Kettering Frailty Index with outcomes of interest
| Characteristic | All patients (n = 79) |
Non-frail (n = 60) |
Frail (n = 19) |
p value* |
|---|---|---|---|---|
| Overall postoperative complications, N (%) | 17 (22) | 11 (19) | 6 (35) | 0.1 |
| Major postoperative complications, N (%) | 21 (26) | 13 (22) | 8 (42) | 0.08 |
| Minor postoperative complications, N (%) | 12 (15) | 9 (15) | 3 (16) | 0.9 |
| Readmission at 30 days, N (%) | 4 (5) | 3 (5) | 1 (5) | 0.9 |
| Mortality within 30 days, N (%) | 5 (6) | 3 (5) | 2 (10) | 0.5 |
| Hospital LOS (days), median (IQR) | 7 (4–10) | 7 (4–9.5) | 7 (4–10) | 0.9 |
| ICU admission, N (%) | 19 (24) | 10 (17) | 9 (47) | 0.006 |
| Estimated blood loss (ml), median (IQR) | 300 (150–500) | 250 (150–500) | 300 (110–600) | 0.6 |
| Operative time (h), median (IQR) | 180 (130–240) | 180 (132.5–240) | 180 (130–240) | 0.9 |
| Intraoperative complications, N (%) | 21 (26) | 13 (22) | 7 (41) | 0.1 |
Abbreviations: LOS, length of stay; ICU, intensive care unit
*p value < 0.05 is significant
In univariate analysis, the MSK-FI was associated with postoperative complications [OR 1.57 (95% CI 1.04–2.37), p = 0.03] and ICU admission [OR 2.05 (95% CI 1.30–3.23), p = 0.002], but not with readmission rate [OR 1.29 (95% CI 0.65–2.59), p = 0.5], postoperative mortality [OR 1.02 (95% CI 0.51–2.00), p = 0.9], hospital stay [β 0.60 (95% CI − 1.19–2.41), p = 0.5], or overall survival [HR 0.3 (95% CI 0.87–1.54), p = 0.3]. On multivariable analysis, the frailty index was independently associated with postoperative complications [OR 1.54 (95% CI 1.02–2.34), p = 0.04] and ICU admission rates [OR 1.97 (95% CI 1.23–3.16), p = 0.004], after adjusting by age, BMI, albumin, surgical complexity, and the stage of the disease. Tables 3 and 4 summarize the findings of our logistic regression analysis.
Table 3.
Association of Memorial Sloan Kettering Frailty Index with postoperative outcomes
| Variable | Estimate (95% CI) | p value* |
|---|---|---|
| Overall postoperative complications* | 1.56 (1.04–2.36) | 0.03 |
| Major postoperative complications* | 1.37 (0.94–2.00) | 0.09 |
| Minor postoperative complications* | 1.35 (0.86–2.10) | 0.18 |
| Readmission within 30 days* | 1.29 (0.64–2.59) | 0.5 |
| 30-day mortality* | 1.02 (0.51–2.00) | 0.9 |
| ICU admission* | 2.05 (1.30–3.23) | 0.002 |
| Length of stay** | 0.60 (− 1.17–2.38) | 0.5 |
| Overall survival*** | 1.16 (0.87–1.54) | 0.3 |
*Logistic regression
**Linear regression
***Cox regression analysis
Table 4.
Logistic regression analysis for overall postoperative complications and ICU admission
| Variable | Univariate | Multivariate | ||||
|---|---|---|---|---|---|---|
| OR | CI 95% | p value* | OR | CI 95% | p value** | |
| Predictors for overall postoperative complications | ||||||
| Frail status ± | 1.57 | 1.04–2.37 | 0.03 | 1.54 | 1.02–2.34 | 0.04 |
| Age | 1.04 | 0.95–1.13 | 0.3 | – | – | – |
| BMI | 0.94 | 0.83–1.13 | 0.4 | – | – | – |
| Albumin | 0.60 | 0.28–1.29 | 0.2 | – | – | – |
| CCI | 1.34 | 1.01–1.77 | 0.04 | 1.26 | 0.93–1.69 | 0.1 |
| Surgical complexity | 1.70 | 0.66–4.38 | 0.3 | – | – | – |
| Stage IV | 1.22 | 0.11–12.6 | 0.8 | – | – | – |
| Predictors for ICU admission | ||||||
| Frail status ± | 2.05 | 1.30–3.23 | 0.002 | 1.97 | 1.23–3.16 | 0.004 |
| Age | 1.05 | 0.96–1.13 | 0.2 | – | – | – |
| BMI | 1.01 | 0.90–1.13 | 0.8 | – | – | – |
| Albumin | 0.61 | 0.29–1.29 | 0.2 | – | – | – |
| CCI | 1.24 | 0.95–1.63 | 0.1 | 0.10 | 0.81–1.49 | 0.5 |
| Surgical complexity | 1.28 | 0.52–3.17 | 0.6 | – | – | – |
| Stage IV | 1.05 | 0.10–10.7 | 0.9 | – | – | – |
Abbreviations: BMI, body mass index; CCI, Charlson comorbidity index
*p value < 0.2 used in multivariate regression model
**p value < 0.05 is significant
± Frail status is defined as MSK-FI ≥ 3
Discussion
To this date, evidence-based recommendations concerning the precise considerations that should be taken with the older adult population undergoing oncological surgery are very limited. In this retrospective study, we analyzed a group of 79 women with advance EOC who underwent PDS or IDS, of which more than 25% were identified as frail. The frequency of the outcomes of interest was not different between frail and non-frail patients, including postoperative complications [6 (35%) vs 11 (19%), p = 0.1] and readmission rate [1 (5%) vs 3 (5%), p = 0.9]. However, on univariable analysis, the frailty index was associated with postoperative complications [OR 1.57 (95% CI 1.04–2.37), p = 0.03] and ICU admission [OR 1.97 (95% CI 1.30–3.23), p = 0.002]. Results were similar on multivariable analysis; frailty index continued to be an independent predictor for ICU admission rates [OR 1.97 (95% CI 1.23–3.16), p = 0.004] and postoperative complications [OR 1.54 (95% CI 1.02–2.34), p = 0.04]. Our results are consistent with previously published work [6, 8, 10].
In our study, we found that frail patients (as defined by MSK-FI score ≥ 3) (1) require more admission to the ICU within 30 days of surgery (p = 0.006), (2) were less likely to have undergone PDS (p = 0.01), and (3) were significantly more like to have suboptimal surgical resection (p ≤ 0.001). These findings are consistent with previous reports [11, 12] and support the need for further frailty assessment in the preoperative workup of these groups of patients. Identification of this characteristic will facilitate functional optimization and individualized interventions to improve the preoperative status and also allow them to give more realistic expectations to the patients and families [6]. We believe that frailty should play an important role in surgical decision-making in patients with advance EOC, and the MSK-FI can be used for this purpose and could be used when providing enhanced postoperative care for frail patients, with the aim of reducing hospital LOS and postoperative ICU admissions.
We recognize several limitations inherent to be a single-institution study conducted at a third-level academic center. We did not include a correlation analysis of the MSK-FI with other methods for preoperative assessment and performance scales such as the Karnofsky and the Eastern Cooperative Oncology Group (ECOG) scale. The association of frailty with the interest outcomes could have been underestimated in our study, given the surgical specialization, patient volume, and other resources that may not be present at other institutions with limited resources. Another limitation is that we did not evaluate the effect of frailty in patient functionality and quality of life in the long term. Future studies should assess the validity and utility of the MSK-FI for these issues.
Despite these limitations, our study demonstrated that frailty is a measurable, objective clinical syndrome that has an impact on postoperative outcomes in advance EOC and should be considered when decision-making about treatments and counseling patients. The MSK-FI was shown to be associated with previously validated geriatric assessment (GA) outcomes and postoperative outcomes in older patients with cancer [8–13]. This tool may be used to perioperatively assess frailty among older patients with cancer.
Conclusions
Frailty, based on the Memorial Sloan Kettering Frailty Index, is associated with adverse postoperative outcomes in older women with advanced ovarian cancer, suggesting that MSK-FI can improve the predictive ability of current surgical assessment tools. Future studies are needed to externally validate this tool and to explore its use for postoperative health care and outcomes.
Author Contribution
Study conception and design was made by JLAF, PMV, FUPS, and HMF. Acquisition of data was made by FAC, FRS, EC, and CAHG. Analysis and interpretation of data was made by JLAF, PMV, CMV, and HMF. Drafting of the manuscript was made by JLAF, AAG, HMF, PMV, and CMV. Critical revision and approval of the manuscript was made by JLAF, AAG, FUPS, and HMF.
Declarations
Ethics Approval
This research was approved by our institutional ethics committee.
Consent to Participate
Informed consent was obtained from all the patients involved in the study.
Conflict of Interest
The authors declare no competing interests.
Footnotes
Highlights
• The prevalence of frailty in patients with advanced ovarian cancer is as high as 25%.
• Frailty according to MSK-FI is an independent predictor of postoperative complications.
• Frail patients with advanced ovarian cancer undergoing surgery require more often postoperative ICU care.
• The use of frailty index as part of the armamentarium for preoperative risk assessment allow for medical and functional optimization
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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