Prediction of Discharge Destination following Laryngectomy

Panwar, Aru; Wang, Fangfang; Lindau, Robert; Militsakh, Oleg; Coughlin, Andrew; Smith, Russell; Sayles, Harlan; Lydiatt, Daniel D.; Lydiatt, William M.

doi:10.1177/0194599818792211

Cited by 7 publications

(18 citation statements)

References 14 publications

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“…Not being able to go home from the hospital can be psychologically and logistically challenging for patients and families. As noted by Panwar et al, counseling may increase patient engagement and increase readiness for nonhome discharge 12 . With respect to discharge planning, when we identify high‐risk patients preoperatively (ie, limited or no social supports, likely need for tracheostomy at discharge, increased age, and/or government insurance), we can initiate early discharge planning.…”

Section: Discussionmentioning

confidence: 99%

“…As noted by Panwar et al, counseling may increase patient engagement and increase readiness for nonhome discharge. 12 With respect to discharge planning, when we identify high-risk patients preoperatively (ie, limited or no social supports, likely need for tracheostomy at discharge, increased age, and/or government insurance), we can initiate early discharge planning. This may help with reducing length of stay for a multitude of reasons.…”

Section: Discussionmentioning

confidence: 99%

“…While Panwar et al studied patients with laryngectomy alone, many other studies on this topic included a variety of head and neck surgery and reconstruction. [10][11][12][13] All of the patients in our cohort had tracheostomies placed and had free flap reconstruction of the oral cavity. While the postoperative care of patients who undergo laryngectomies or free flap reconstruction of other head and neck defects is also complex, these patients should be studied independently.…”

Section: Discussionmentioning

confidence: 99%

“…10,11 Panwar et al similarly identified predictors of post–acute care in patients after total laryngectomy using the NSQIP. 12 While these studies were able to analyze demographic and clinical variables in a large number of patients, they were not able to study the impact of other important issues, such as social support and insurance status. In a 2013 study that assessed the impact of age on disposition after head and neck free tissue transfer, Hatcher et al similarly did not look at social factors.…”

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confidence: 99%

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Risk Factors for Post–acute care Following Free Flap Reconstruction of the Oral Cavity

Lepse

Sykes

Kakarala

2020

Otolaryngol.--head neck surg.

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Objective Identify previously unreported factors that predict the need for post–acute care after free flap reconstruction of the oral cavity. Study Design Retrospective cohort study. Setting Single academic medical center. Methods A total of 134 patients with head and neck disease involving the oral cavity underwent free tissue transfer for reconstruction between August 2012 and October 2015. All patients had a tracheostomy placed at the time of surgery. Data were collected, including demographics, perioperative risk factors, and social variables. Univariate and multivariate logistic regression were used to identify risk factors for needing post–acute care. Results Of 134 patients, 37 (28%) required post–acute care upon discharge, and 97 of 134 (72%) were discharged home with assistance. Multivariate logistic regression revealed that lack of family support (adjusted odds ratio [AOR], 32.12; 95% CI, 13.75-274.90; P = .002), tracheostomy tube at discharge (AOR, 13.70; 95% CI, 3.20-58.44; P < .001), government insurance (AOR, 3.85; 95% CI, 1.13-13.11; P = .031), hospital stay >10 days (AOR, 3.52; 95% CI, 1.25-9.90; P = .017), and increasing age (AOR, 1.11; 95% CI, 1.04-1.18; P = .003) were significantly associated with post–acute care need. Conclusion: Lack of family support, tracheostomy tube at discharge, government insurance, hospital stay >10 days, and increasing age are independently associated with the need for post–acute care following free flap reconstruction of the oral cavity. Physicians, social workers, and nurse case managers are positioned to identify patients at high risk for needing post–acute care and to reduce the duration of hospitalizations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Risk Factors for Post–acute care Following Free Flap Reconstruction of the Oral Cavity

Lepse

Sykes

Kakarala

2020

Otolaryngol.--head neck surg.

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“…Although Panwar et al 26 and White et al, 27 have previously used traditional statistics to investigate LOS and DNHF outcomes in NSQIP's HN patients, this study's novelty lies in its ML approach (both classification and regression models) and publishing these as publicly available web‐based interfaces where new input data can produce real‐time predictions. Despite their limitations, these ML models can serve as proof‐of‐concept applications that can be improved with future multi‐institutional investigations.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

et al. 2020

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Background This study develops machine learning (ML) algorithms that use preoperative‐only features to predict discharge‐to‐nonhome‐facility (DNHF) and length‐of‐stay (LOS) following complex head and neck surgeries. Methods Patients undergoing laryngectomy or composite tissue excision followed by free tissue transfer were extracted from the 2005 to 2017 NSQIP database. Results Among the 2786 included patients, DNHF and mean LOS were 421 (15.1%) and 11.7 ± 8.8 days. Four classification models for predicting DNHF with high specificities (range, 0.80‐0.84) were developed. The generalized linear and gradient boosting machine models performed best with receiver operating characteristic (ROC), accuracy, and negative predictive value (NPV) of 0.72‐0.73, 0.75‐0.76, and 0.88‐0.89. Four regression models for predicting LOS in days were developed, where all performed similarly with mean absolute error and root mean‐squared errors of 3.95‐3.98 and 5.14‐5.16. Both models were developed into an encrypted web‐based interface: https://uci-ent.shinyapps.io/head-neck/. Conclusion Novel and proof‐of‐concept ML models to predict DNHF and LOS were developed and published as web‐based interfaces.

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Factors Impacting Discharge Destination Following Head and Neck Microvascular Reconstruction

et al. 2022

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Objective: Determine which variables impact postoperative discharge destination following head and neck microvascular free flap reconstruction.Study Design: Retrospective review of prospectively collected databases. Methods: Consecutive patients undergoing head and neck microvascular free flap reconstruction between January 2010 and December 2019 (n = 1972) were included. Preoperative, operative and postoperative variables were correlated with discharge destination (home, skilled nursing facility [SNF], rehabilitation facility, death).Results: The mean age of patients discharged home was lower (60 SD AE 13, n = 1450) compared to those discharged to an SNF (68 SD AE 14, n = 168) or a rehabilitation facility (71 SD AE 14, n = 200; p < 0.0001). Operative duration greater than 10 h correlated with a higher percentage of patients being discharged to a rehabilitation or SNF (25% vs. 15%; p < 0.001). Patients were less likely to be discharged home if they had a known history of cardiac disease (71% vs. 82%; p < 0.0001). Patients were less likely to be discharged home if they experienced alcohol withdrawal (67% vs. 80%; p = 0.006), thromboembolism (59% vs. 80%; p = 0.001), a pulmonary complication (46% vs. 81%; p < 0.0001), a cardiac complication (46% vs. 80%; p < 0.0001), or a cerebral vascular event (25% vs. 80%; p < 0.0001). There was no correlation between discharge destination and occurrence of postoperative wound infection, salivary fistula, partial tissue necrosis or free flap failure. Thirty-day readmission rates were similar when stratified by discharge destination.Conclusion: There was no correlation with the anatomic site, free flap donor selection, or free flap survival and discharge destination. Patient age, operative duration and occurrence of a medical complication postoperatively did correlate with discharge destination.

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Prediction of Discharge Destination following Laryngectomy

Abstract: Patients with advanced age, functional dependence, cardiac failure, and steroid dependence may benefit from preemptive counseling and discharge planning in anticipation of postlaryngectomy discharge to intermediate-care facilities.

Cited by 7 publications

References 14 publications

Risk Factors for Post–acute care Following Free Flap Reconstruction of the Oral Cavity

Risk Factors for Post–acute care Following Free Flap Reconstruction of the Oral Cavity

Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

Factors Impacting Discharge Destination Following Head and Neck Microvascular Reconstruction

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