Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System. METHODS: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000 short snippets of clinical notes, and annotated MM stage at MM treatment initiation for 200 patients. These were allocated into snippet- and patient-level development and validation sets. We developed MM stage extraction and roll-up algorithms within the development sets. After the algorithms were finalized, we validated them using standard measures in held-out validation sets. RESULTS: We developed algorithms for three different MM staging systems that have been in widespread use (Revised International Staging System [R-ISS], International Staging System [ISS], and Durie-Salmon [DS]) and for stage reported without a clearly defined system. Precision and recall were uniformly high for MM stage at the snippet level, ranging from 0.92 to 0.99 for the different MM staging systems. Performance in identifying for MM stage at treatment initiation at the patient level was also excellent, with precision of 0.92, 0.96, 0.90, and 0.86 and recall of 0.99, 0.98, 0.94, and 0.92 for R-ISS, ISS, DS, and unclear stage, respectively. CONCLUSION: Our MM stage extraction algorithm uses rule-based NLP and data aggregation to accurately measure MM stage documented in oncology notes and pathology reports in VA's national EHR system. It may be adapted to other systems where MM stage is recorded in clinical notes.

The performance status gap in immunotherapy for frail patients with advanced non-small cell lung cancer.

PURPOSE: In advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitor (ICI) monotherapy is often preferred over intensive ICI treatment for frail patients and those with poor performance status (PS). Among those with poor PS, the additional effect of frailty on treatment selection and mortality is unknown. METHODS: Patients in the veterans affairs national precision oncology program from 1/2019-12/2021 who received first-line ICI for advanced NSCLC were followed until death or study end 6/2022. Association of an electronic frailty index with treatment selection was examined using logistic regression stratified by PS. We also examined overall survival (OS) on intensive treatment using Cox regression stratified by PS. Intensive treatment was defined as concurrent use of platinum-doublet chemotherapy and/or dual checkpoint blockade and non-intensive as ICI monotherapy. RESULTS: Of 1547 patients receiving any ICI, 66.2% were frail, 33.8% had poor PS (≥ 2), and 25.8% were both. Frail patients received less intensive treatment than non-frail patients in both PS subgroups (Good PS: odds ratio [OR] 0.67, 95% confidence interval [CI] 0.51 - 0.88; Poor PS: OR 0.69, 95% CI 0.44 - 1.10). Among 731 patients receiving intensive treatment, frailty was associated with lower OS for those with good PS (hazard ratio [HR] 1.53, 95% CI 1.2 - 1.96), but no association was observed with poor PS (HR 1.03, 95% CI 0.67 - 1.58). CONCLUSION: Frail patients with both good and poor PS received less intensive treatment. However, frailty has a limited effect on survival among those with poor PS. These findings suggest that PS, not frailty, drives survival on intensive treatment.

Tailoring Therapy in Older Adults With Hematologic Malignancies.

Hematologic malignancies most often present in the sixth or seventh decade of life. Even so, many older adults may be unable to tolerate standard chemotherapy or require supplementary care or dose adjustments to do so. Both in community and academic centers, geriatric assessment (GA) can be used to improve the care of older adults with blood cancers. For example, hematologic oncologists can use GA to guide treatment selection, adjusting for patient frailty and goals, as well as prompt initiation of enhanced supportive care. After initial therapy, GA can improve the identification of older adults with aggressive myeloid malignancies who would benefit from hematopoietic cell transplantation (HCT), inform shared decision making, as well as allow transplanters to tailor conditioning regimen, donor selection, graft-versus-host disease prophylaxis, and pre- and post-HCT treatments. As in HCT, GA can improve the care of older patients with relapsed lymphoma or multiple myeloma eligible for chimeric antigen receptor-T therapy, identifying patients at higher risk for toxicity and providing a baseline for subsequent neurocognitive testing. Here, we review the data supporting GA for the care of older adults with blood cancers, from the community to the academic center. In addition, we explore future directions to optimize outcomes for older adults with hematologic malignancies.

Validity and Reliability of a Smartphone Application for Home Measurement of Four-Meter Gait Speed in Older Adults.

The four-meter gait speed (4MGS) is a recommended physical performance test in older adults but is challenging to implement clinically. We developed a smartphone application (App) with a four-meter ribbon for remote 4MGS testing at home. This study aimed to assess the validity and reliability of this smartphone App-based assessment of the home 4MGS. We assessed the validity of the smartphone App by comparing it against a gold standard video assessment of the 4MGS conducted by study staff visiting community-dwelling older adults and against the stopwatch-based measurement. Moreover, we assessed the test-retest reliability in two supervised sessions and three additional sessions performed by the participants independently, without staff supervision. The 4MGS measured by the smartphone App was highly correlated with video-based 4MGS (r = 0.94), with minimal differences (mean = 0.07 m/s, ± 1.96 SD = 0.12) across a range of gait speeds. The test-retest reliability for the smartphone App 4MGS was high (ICC values: 0.75 to 0.93). The home 4MGS in older adults can be measured accurately and reliably using a smartphone in the pants pocket and a four-meter strip of ribbon. Leveraging existing technology carried by a significant portion of the older adult population could overcome barriers in busy clinical settings for this well-established objective mobility test.

Baseline correlates of frailty and its association with survival in United States veterans with acute myeloid leukemia.

Frailty is an important construct to measure in acute myeloid leukemia (AML). We used the Veterans Affairs Frailty Index (VA-FI) - calculated using readily available data within the VA's electronic health records - to measure frailty in U.S. veterans with AML. Of the 1166 newly diagnosed and treated veterans with AML between 2012 and 2022, 722 (62%) veterans with AML were classified as frail (VA-FI > 0.2). At a median follow-up of 252.5 days, moderate-severely frail veterans had significantly worse survival than mildly frail, and non-frail veterans (median survival 179 vs. 306 vs. 417 days, p < .001). Increasing VA-FI severity was associated with higher mortality. A model with VA-FI in addition to the European LeukemiaNet (ELN) risk classification and other covariates statistically outperformed a model containing the ELN risk and other covariates alone (p < .001). These findings support the VA-FI as a tool to expand frailty measurement in research and clinical practice for informing prognosis in veterans with AML.

Construct validity of the electronic Veterans Affairs Frailty Index against clinician frailty assessment.

BACKGROUND: Electronic frailty indices (eFIs) can expand measurement of frailty in research and practice and have demonstrated predictive validity in associations with clinical outcomes. However, their construct validity is less well studied. We aimed to assess the construct validity of the VA-FI, an eFI developed for use in the U.S. Veterans Affairs Healthcare System. METHODS: Veterans who underwent comprehensive geriatric assessments between January 31, 2019 and June 6, 2022 at VA Boston and had sufficient data documented for a comprehensive geriatric assessment-frailty index (CGA-FI) were included. The VA-FI, based on diagnostic and procedural codes, and the CGA-FI, based on geriatrician-measured deficits, were calculated for each patient. Geriatricians also assessed the Clinical Frailty Scale (CFS), functional status (ADLs and IADLs), and 4-meter gait speed (4MGS). RESULTS: A total of 132 veterans were included, with median age 81.4 years (IQR 75.8-88.7). Across increasing levels of VA-FI (<0.2; 0.2-0.4; >0.4), mean CGA-FI increased (0.24; 0.30; 0.40). The VA-FI was moderately correlated with the CGA-FI (r 0.45, p < 0.001). Every 0.1-unit increase in the VA-FI was associated with an increase in the CGA-FI (linear regression beta 0.05; 95% confidence interval [CI] 0.03-0.06), higher CFS category (ordinal regression OR 1.69; 95% CI 1.24-2.30), higher odds of ADL dependency (logistic regression OR 1.59; 95% CI 1.20-2.11), IADL dependency (logistic regression OR 1.68; 95% CI 1.23-2.30), and a decrease in 4MGS (linear regression beta -0.07, 95% CI -0.12 to -0.02). All models were adjusted for age and race, and associations held after further adjustment for the Charlson Comorbidity Index. CONCLUSION: Our results demonstrate the construct validity of the VA-FI through its associations with clinical measures of frailty, including summary frailty measures, functional status, and objective physical performance. Our findings complement others' in showing that eFIs can capture functional and mobility domains of frailty beyond just comorbidity and may be useful to measure frailty among populations and individuals.

More intensive therapy as more effective treatment for frail patients with multiple myeloma [corrected].

Although randomized controlled trial data suggest that the more intensive triplet bortezomib-lenalidomide-dexamethasone (VRd) is superior to the less intensive doublet lenalidomide-dexamethasone (Rd) in patients newly diagnosed with multiple myeloma (MM), guidelines have historically recommended Rd over VRd for patients who are frail and may not tolerate a triplet. We identified 2573 patients (median age, 69.7 years) newly diagnosed with MM who were initiated on VRd (990) or Rd (1583) in the national US Veterans Affairs health care System from 2004 to 2020. We measured frailty using the Veterans Affairs Frailty Index. To reduce imbalance in confounding, we matched patients for MM stage and 1:1 based on a propensity score. Patients who were moderate-severely frail had a higher prevalence of stage III MM and myeloma-related frailty deficits than patients who were not frail. VRd vs Rd was associated with lower mortality (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.70-0.94) in the overall matched population. Patients who were moderate-severely frail demonstrated the strongest association (HR 0.74; 95% CI, 0.56-0.97), whereas the association weakened in those who were mildly frail (HR, 0.80; 95% CI, 0.61-1.05) and nonfrail (HR, 0.86; 95% CI, 0.67-1.10). VRd vs Rd was associated with a modestly higher incidence of hospitalizations in the overall population, but this association weakened in patients who were moderate-severely frail. Our findings confirm the benefit of VRd over Rd in US veterans and further suggest that this benefit is strongest in patients with the highest levels of frailty, arguing that more intensive treatment of myeloma may be more effective treatment of frailty itself.

Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.

BACKGROUND: Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. METHODS: In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs. FINDINGS: Using the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851-0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829-0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812-0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869-0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96-1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model. INTERPRETATION: The ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed. FUNDING: National Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.

Benefits and Barriers of Technology for Home Function and Mobility Assessment: Perspectives of Older Patients With Blood Cancers, Caregivers, and Clinicians.

PURPOSE: Advances in digital health technology can overcome barriers to measurement of function and mobility for older adults with blood cancers, but little is known about how older adults perceive such technology for use in their homes. METHODS: To characterize potential benefits and barriers associated with using technology for home functional assessment, we conducted three semistructured focus groups (FGs) in January 2022. Eligible patients came from the Older Adult Hematologic Malignancies Program at Dana-Farber Cancer Institute (DFCI), which includes adults 73 years and older enrolled during their initial consult with their oncologist. Eligible caregivers were 18 years and older and identified by enrolled patients as their primary caregiver. Eligible clinicians were practicing DFCI hematologic oncologists, nurse practitioners, or physician assistants with ≥2 years of clinical experience. A qualitative researcher led thematic analysis of FG transcripts to identify key themes. RESULTS: Twenty-three participants attended the three FGs: eight patients, seven caregivers, and eight oncology clinicians. All participants valued function and mobility assessments and felt that technology could overcome barriers to their measurement. We identified three themes related to potential benefits: making it easier for oncology teams to consider function and mobility; providing standardized, objective data; and facilitating longitudinal data. We also identified four themes related to barriers to home functional assessment: concerns related to privacy and confidentiality, burden of measuring additional patient data, challenges in operating new technology, and concerns related to data improving care. CONCLUSION: These data suggest that specific concerns raised by older patients, caregivers, and oncology clinicians must be addressed to improve acceptability and uptake of technology used to measure function and mobility in the home.

Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System.

BACKGROUND: We aimed to evaluate and compare the performance of multiple myeloma (MM) selection algorithms for use in Veterans Affairs (VA) research. METHODS: Using the VA Corporate Data Warehouse (CDW), the VA Cancer Registry (VACR), and VA pharmacy data, we randomly selected 500 patients from 01/01/1999 to 06/01/2021 who had (1) either one MM diagnostic code OR were listed in the VACR as having MM AND (2) at least one MM treatment code. A team reviewed oncology notes for each veteran to annotate details regarding MM diagnosis and initial treatment within VA. We evaluated inter-annotator agreement and compared the performance of four published algorithms (two developed and validated external to VA data and two used in VA data). RESULTS: A total of 859 patients were reviewed to obtain 500 patients who were annotated as having MM and initiating MM treatment in VA. Agreement was high among annotators for all variables: MM diagnosis (98.3% agreement, Kappa = 0.93); initial treatment in VA (91.8% agreement; Kappa = 0.77); and initial treatment classification (87.6% agreement; Kappa = 0.86). VA Algorithms were more specific and had higher PPVs than non-VA algorithms for both MM diagnosis and initial treatment in VA. We developed the "VA Recommended Algorithm," which had the highest PPV among all algorithms in identifying patients diagnosed with MM (PPV = 0.98, 95% CI = 0.95-0.99) and in identifying patients who initiated their MM treatment in VA (PPV = 0.93, 95% CI = 0.90-0.96). CONCLUSION: Our VA Recommended Algorithm optimizes sensitivity and PPV for cohort selection and treatment classification.

Perioperative Esophagectomy Outcomes in Older Esophageal Cancer Patients in Two Different Time Eras.

To investigate perioperative outcomes of esophagectomies by age groups. Retrospective analysis of esophageal cancer patients undergoing esophagectomy from 2005 to 2020 at a single academic institution. Baseline characteristics and outcomes were analyzed by 3 age groups: <70, 70-79, and ≥80 years-old. Sub-analysis was done for 2 time periods: 2005-2012 and 2013-2020. Of 1135 patients, 789 patients were <70, 294 were 70-79, and 52 were ≥80 years-old. Tumor characteristics, and operative technique were similar, except positive longitudinal margins rates (all <3%) (P = 0.008). Older adults experienced increased complications (53.6% vs 69.7% vs 65.4% respectively; P < 0.001) attributable to grade II complications (41.4% vs 62.2% vs 63.5% respectively; P < 0.001). Hospital length of stay (LOS) and rehabilitation requirements were higher in older adults (both P < 0.05). 30-day readmissions, reoperation, and 30-day mortality rates (all <2%) showed no association with age group. Overall complications, LOS, discharge disposition and re-operative rates improved from 2005 to 2012 to 2013-2020 for all (P < 0.05). Increasing age was an independent risk factor for cardiovascular complications (OR 1.7, 95% CI 1.23-2.46 for ages 70-79 and OR 2.7, 95% CI 1.37-5.10 for ages ≥80 ), inpatient rehabilitation (OR 3.3, 95% CI 2.26-5.05 for ages 70-79 and OR 12.1 95% CI 5.83-25.04 for ages ≥80), and prolonged LOS (OR 1.64 95% CI 1.16-2.31 for ages 70-79 and OR 3.6 95% CI 1.71-7.67 for ≥80. After adjusting for time period, older age remained associated with complications (P < 0.05). Highly selected older adults at a large volume esophagectomy center can undergoesophagectomy with increased minor complication and rehabilitation needs.

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study.

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively; discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively; and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.

Validation of the Pictorial Fit-Frail Scale in a Thoracic Surgery Clinic.

OBJECTIVE: Examine feasibility and construct validity of Pictorial Fit-Frail scale (PFFS) for the first time in older surgical patients. BACKGROUND: The PFFS uses visual images to measure health state in 14 domains and has been previously validated in outpatient geriatric clinics. METHODS: Patients ≥65 year-old who were evaluated in a multidisciplinary thoracic surgery clinic from November 2020 to May 2021 were prospectively included. Patients completed an in-person PFFS and Vulnerable Elders Survey (VES-13) during their visit, and a frailty index was calculated from the PFFS (PFFStrans). A geriatrician performed a comprehensive geriatric assessment (CGA) either in-person or virtually, from which a Frailty Index (FI-CGA) and Frailty Questionnaire (FRAIL) scale were obtained. To assess the validity of the PFFS in this population, the Spearman rank correlations (r spearman ) between PFFS trans and VES-13, FI-CGA, FRAIL were calculated. RESULTS: All 49 patients invited to participate agreed, of which 46/49 (94%) completed the PFFS so a score could be calculated. The majority of patients (59%) underwent an in-person CGA and the reminder (41%) a virtual CGA. The cohort was mainly female (59.0%), with a median age of 77 (range: 67-90). The median PFFS trans was 0.27 (interquartile range [IQR] 0.12-0.34), PFFS was 11 (IQR 5-14), and 0.24 (IQR 0.13-0.32) for FI-CGA. We observed a strong correlation between the PFFS trans and FI-CGA (r spearman = 0.81, P < 0.001) and a moderate correlation between PFFS trans and VES-13 and FRAIL score (r spearman = 0.68 and 0.64 respectively, P < 0.001). CONCLUSIONS: PFFS had good feasibility and construct validity among older surgical patients when compared to previously validated frailty measurements.

Association of Polypharmacy and Potentially Inappropriate Medications With Frailty Among Older Adults With Blood Cancers.

BACKGROUND: Polypharmacy and potentially inappropriate medications (PIMs) are common among older adults with blood cancers, but their association with frailty and how to manage them optimally remain unclear. PATIENTS AND METHODS: From 2015 to 2019, patients aged ≥75 years presenting for initial oncology consult underwent screening geriatric assessment. Patients were determined to be robust, prefrail, or frail via deficit accumulation and phenotypic approaches. We quantified each patient's total number of medications and PIMs using the Anticholinergic Risk Scale (ARS) and a scale we generated using the NCCN Medications of Concern called the Geriatric Oncology Potentially Inappropriate Medications (GO-PIM) scale. We assessed cross-sectional associations of PIMs with frailty in multivariable regression models adjusting for age, gender, and comorbidity. RESULTS: Of 785 patients assessed, 603 (77%) were taking ≥5 medications and 421 (54%) were taking ≥8 medications; 201 (25%) were taking at least 1 PIM based on the ARS and 343 (44%) at least 1 PIM based on the GO-PIM scale. Among the 468 (60%) patients on active cancer treatment, taking ≥8 medications was associated with frailty (adjusted odds ratio [aOR], 2.82; 95% CI, 1.92-4.17). With each additional medication, the odds of being prefrail or frail increased 8% (aOR, 1.08; 95% CI, 1.04-1.12). With each 1-point increase on the ARS, the odds of being prefrail or frail increased 19% (aOR, 1.19; 95% CI, 1.03-1.39); with each additional PIM based on the GO-PIM scale, the odds increased 65% (aOR, 1.65; 95% CI, 1.34-2.04). CONCLUSIONS: Polypharmacy and PIMs are prevalent among older patients with blood cancers; taking ≥8 medications is strongly associated with frailty. These data suggest careful medication reconciliation for this population may be helpful, and deprescribing when possible is high-yield, especially for PIMs on the GO-PIM scale.

Peripheral blood CD3+ T-cell gene expression biomarkers correlate with clinical frailty in patients with haematological malignancies.

Older patients with cancer often receive treatment regimens based on their age without considering other objective factors that may influence outcomes. Assessment of frailty can identify older patients who are robust and therefore more likely to benefit from intensive treatment, or conversely, frail and might instead be offered alternative approaches. However, such assessment requires specialised training and dedicated clinical resources. Alternative quantitative biomarkers associated with frailty are lacking. Here, we asked if expression signatures of 74 immune cell, ageing, and senescence-related messenger RNAs in purified peripheral blood T cells could identify associations with clinical frailty in patients with haematological malignancies. We studied 69 patients between the ages of 36 and 92 years (median 76 years) with leukaemia, lymphoma, or multiple myeloma, across two institutions. Expression of four genes (aryl hydrocarbon receptor [AHR], CD27, CD28, and interleukin-2 receptor subunit alpha [IL2RA; CD25]) in T cells was associated with frailty, independent of age. An expression-based regression model had 76% sensitivity and 90% specificity to assign a patient as robust. These data identify measurable peripheral blood correlates of clinical frailty and suggest biomarkers for future prospective assessment.

Thoracic surgery with geriatric assessment and collaboration can prepare frail older adults for lung cancer surgery.

BACKGROUND AND OBJECTIVES: We assessed frailty, measured by a comprehensive geriatric assessment-based frailty index (FI-CGA), and its association with postoperative outcomes among older thoracic surgical patients. METHODS: Patients aged ≥65 years evaluated in the geriatric-thoracic clinic between June 2016 through May 2020 who underwent lung surgery were included. Frailty was defined as FI-CGA > 0.2, and "occult frailty", a level not often recognized by surgical teams, as 0.2 < FI-CGA < 0.4. A qualitative analysis of geriatric interventions was performed. RESULTS: Seventy-three patients were included, of which 45 (62%) were nonfrail and 28 (38%) were frail. "Occult frailty" was present in 23/28 (82%). Sixty-one (84%) had lung malignancy. Geriatric interventions included delirium management, geriatric-specific pain and bowel regimens, and frailty optimization. More sublobar resections versus lobectomies (61% vs. 25%) were performed among frail patients. Frailty was not significantly associated with overall complications (odds ratio [OR]: 2.4; 95% confidence interval [CI]: 0.88-6.44; p = 0.087), major complications (OR: 2.33; 95% CI: 0.48-12.69; p = 0.293), discharge disposition (OR: 2.8; 95% CI: 0.71-11.95; p = 0.141), or longer hospital stay (1.3 more days; p = 0.18). CONCLUSION: Frailty and "occult frailty" are prevalent in patients undergoing lung surgery. However, with integrated geriatric management, these patients can safely undergo surgery.