Association of Preoperative Functional Performance With Outcomes After Surgical Treatment of Head and Neck Cancer: A Clinical Severity Staging System

Comparison of surgical complications after curative surgery in patients with oral cavity squamous cell carcinoma and sarcopenia

BACKGROUND

The study aims to clarify the association of sarcopenia with perioperative and postoperative complications in oral cavity squamous cell carcinoma (OCSCC) patients undergoing curative surgery and to understand the reasons causing the poor oncologic outcomes for OCSCC.

METHODS

We conducted a propensity score matching study to investigate the association of perioperative and postoperative outcomes in OCSCC patients with sarcopenia and without sarcopenia. A retrospective analysis of a large national data set from the Taiwan Cancer Registry Database was conducted. At least two claims for patients with a principal diagnosis of sarcopenia within the 12-month preoperative period were defined as the criteria for sarcopenia diagnosis (ICD-10-CM code M62.84). Sarcopenia was diagnosed through the measurement of low muscle strength and low muscle mass by any one of the patient's attending orthopaedic physician, rehabilitation physician, family medicine specialist or geriatrician. A multivariate logistic regression model was used to calculate the perioperative, and postoperative major complications.

RESULTS

Our final cohort included 16 293 patients with OCSCC (10 862 and 5 431 in the sarcopenia and nonsarcopenia groups, respectively) who were eligible for further analysis. The sarcopenia group was 10.40% female and 89.60% male, and the nonsarcopenia group was 9.74% female and 90.26% male. The mean age ± standard deviation (SD) were 56.44 ± 11.14 and 56.22 ± 11.29 for sarcopenia and nonsarcopenia groups. OCSCC patients with sarcopenia undergoing curative surgery had a significantly higher blood transfusion rate and volume; longer intensive care unit (ICU) stay, and hospital stay; higher postoperative 30-day mortality (adjusted odds ratio [aOR]: 1.12, 95% confidence interval [CI] [1.07, 1.56]) and rates of pneumonia (aOR: 1.34, 95% CI [1.20, 1.50]), acute renal failure (aOR: 1.45, 95% CI [1.12, 1.87]) and septicaemia (aOR: 1.29, 95% CI [1.15, 1.45]); higher postoperative first-year mortality (aOR: 1.18, 95% CI [1.13, 1.51]) and rates of pneumonia (aOR: 1.43, 95% CI [1.30, 1.56]), acute myocardial infarction (aOR: 1.52, 95% CI [1.06, 2.18]) and septicaemia (aOR: 1.29, 95% CI [1.15, 1.45]).

CONCLUSIONS

OCSCC patients with sarcopenia might exhibit more perioperative and surgical complications than those without sarcopenia.

Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer

Importance

Patient-reported symptom burden was recently found to be associated with emergency department use and unplanned hospitalization (ED/Hosp) in patients with head and neck cancer. It was hypothesized that symptom scores could be combined with administrative health data to accurately risk stratify patients.

Objective

To develop and validate a machine learning approach to predict future ED/Hosp in patients with head and neck cancer.

Design, Setting, and Participants

This was a population-based predictive modeling study of patients in Ontario, Canada, diagnosed with head and neck cancer from January 2007 through March 2018. All outpatient clinical encounters were identified. Edmonton Symptom Assessment System (ESAS) scores and clinical and demographic factors were abstracted. Training and test cohorts were randomly generated in a 4:1 ratio. Various machine learning algorithms were explored, including (1) logistic regression using a least absolute shrinkage and selection operator, (2) random forest, (3) gradient boosting machine, (4) k-nearest neighbors, and (5) an artificial neural network. Data analysis was performed from September 2021 to January 2022.

Main Outcomes and Measures

The main outcome was any 14-day ED/Hosp event following symptom assessment. The performance of each model was assessed on the test cohort using the area under the receiver operator characteristic (AUROC) curve and calibration plots. Shapley values were used to identify the variables with greatest contribution to the model.

Results

The training cohort consisted of 9409 patients (mean [SD] age, 63.3 [10.9] years) undergoing 59 089 symptom assessments (80%). The remaining 2352 patients (mean [SD] age, 63.3 [11] years) and 14 193 symptom assessments were set aside as the test cohort (20%). Several models had high predictive accuracy, particularly the gradient boosting machine (validation AUROC, 0.80 [95% CI, 0.78-0.81]). A Youden-based cutoff corresponded to a validation sensitivity of 0.77 and specificity of 0.66. Patient-reported symptom scores were consistently identified as being the most predictive features within models. A second model built only with symptom severity data had an AUROC of 0.72 (95% CI, 0.70-0.74).

Conclusions and Relevance

In this study, machine learning approaches predicted with a high degree of accuracy ED/Hosp in patients with head and neck cancer. These tools could be used to accurately risk stratify patients and may help direct targeted intervention.

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

OBJECTIVE

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients.

DATA SOURCES

PubMed.

REVIEW METHODS

A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review.

CONCLUSIONS

In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care.

IMPLICATIONS FOR PRACTICE

Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

The Utility of Comorbidity Indices in Assessing Head and Neck Surgery Outcomes: A Systematic Review

OBJECTIVE

To evaluate the utility of comorbidity index (CI) scores in predicting outcomes in head and neck surgery (HNS). The CIs evaluated were the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Kaplan-Feinstein Index (KFI), American Society of Anesthesiologists Physical Status (ASA-PS), Adult Comorbidity Evaluation-27 (ACE-27), National Cancer Institute Comorbidity Index (NCI-CI), and the Washington University Head and Neck Comorbidity Index (WUHNCI).

METHODS

We report a systematic review according to the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases (PubMed, Cochrane, and Embase) and manual search of bibliographies identified manuscripts addressing how CI scores related to HNS outcomes.

RESULTS

A total of 116 studies associated CI scores with HNS outcomes. CIs were represented in the literature as follows: ASA-PS (70/116), CCI (39/116), ACE-27 (24/116), KFI (7/116), NCI-CI (3/116), ECI (2/116), and WUHNCI (1/116). The most frequently cited justification for calculating each CI (if provided) was: CCI for its validation in other studies, ACE-27 for its utility in cancer patients, and ECI for its comprehensive design. In general, the CCI and ACE-27 were predictive of mortality in HNS. The ECI was most consistent in predicting >1-year mortality. The ACE-27 and KFI were most consistent in predicting medical complications.

CONCLUSION

Despite inconsistencies in the literature, CIs provide insights into the impact of comorbidities on outcomes in HNS. These scores should be employed as an adjunct in the preoperative assessment of HNS patients. Comparative studies are needed to identify indices that are most reliable in predicting HNS outcomes.

LEVEL OF EVIDENCE

NA Laryngoscope, 2021.

Predictors of surgical readmission, unplanned hospitalization and emergency department use in head and neck oncology: A systematic review

OBJECTIVE

To identify predictors of unplanned hospitalization and emergency department (ED) use among head and neck oncology patients.

METHODS

Peer reviewed publications were identified through a systematic search of MEDLINE, Embase and Cochrane CENTRAL. Studies describing a cohort of HNC patients that detailed predictors of unplanned hospitalization or ED use in risk-adjusted models were eligible for inclusion. The methodologic quality of included studies was assessed using the Quality In Prognostic Studies (QUIPS) tool and an adapted version of the GRADE framework.

RESULTS

Of the 932 articles identified, 39 studies met our inclusion criteria with 31/39 describing predictors of surgical readmission and 10/39 describing predictors of ED use or unplanned hospitalization during radiation/chemoradiation treatment. Risk factors were classified into either 'patient-related', 'cancer severity' or 'process' factors. In the subset of studies looking at readmission following surgery wound complications (10/14 studies), presence of comorbidity (16/28 studies), low socioeconomic status (8/17 studies), cancer stage (9/14 studies), and prolonged hospital stay (7/18 studies) were the variables most frequently associated with readmission on multivariable analysis. Presence of comorbidity (6/10) and chemotherapy use (4/10) were more frequently associated with ED use and unplanned hospitalization.

CONCLUSIONS

Several consistent predictors have been identified across a variety of studies. This work is a critical first step towards the development of readmission and ED prediction models. It also enables meaningful comparison of hospital readmission rates with risk adjustment in HNC patients.

Inadequate Nutrition Coverage in Outpatient Cancer Centers: Results of a National Survey

Cancer-related malnutrition is associated with poor health outcomes, including decreased tolerance to cancer therapy, greater treatment toxicities, and increased mortality. Medical nutrition therapy (MNT) optimizes clinical outcomes, yet registered dietitian nutritionists (RDNs), the healthcare professionals specifically trained in MNT, are not routinely employed in outpatient cancer centers where over 90% of all cancer patients are treated. The objective of this study was to evaluate RDN staffing patterns, nutrition services provided in ambulatory oncology settings, malnutrition screening practices, and referral and reimbursement practices across the nation in outpatient cancer centers. An online questionnaire was developed by the Oncology Nutrition Dietetic Practice Group (ON DPG) of the Academy of Nutrition and Dietetics and distributed via the ON DPG electronic mailing list. Complete data were summarized for 215 cancer centers. The mean RDN full-time equivalent (FTE) for all centers was 1.7 ± 2.0. After stratifying by type of center, National Cancer Institute-Designated Cancer Centers (NCI CCs) employed a mean of 3.1 ± 3.0 RDN FTEs compared to 1.3 ± 1.4 amongst non-NCI CCs. The RDN-to-patient ratio, based on reported analytic cases, was 1 : 2,308. Per day, RDNs evaluated and counseled an average of 7.4 ± 4.3 oncology patients. Approximately half (53.1%) of the centers screened for malnutrition, and 64.9% of these facilities used a validated malnutrition screening tool. The majority (76.8%) of centers do not bill for nutrition services. This is the first national study to evaluate RDN staffing patterns, provider-to-patient ratios, and reimbursement practices in outpatient cancer centers. These data indicate there is a significant gap in RDN access for oncology patients in need of nutritional care.