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Laryngectomy

Laryngectomy is a surgical procedure in which the larynx (voice box) is removed, often as a treatment for laryngeal cancer.
This procedure can impact a person's ability to speak and breathe normally.
The PubCompare.ai platform can help streamline Laryngectomy research by providing AI-powered analysis to discover and compare the latest protocols from literature, preprints, and patents.
Leveraging this platform can help researchers identify the best solutions to optimize Laryngectomy research and improve outcomes for patients undergoing this procedure.

Most cited protocols related to «Laryngectomy»

1
Estimating Cancer Burden: GBD Methodology
Cited 10 times
The GBD cancer mortality and YLL estimation process included 2 primary steps (eFigure 2 in the Supplement), beginning with the estimation of cancer MIRs, which provide an association between mortality and incidence estimation, maximizing data availability. The MIRs were modeled using a space-time Gaussian process regression approach26 (link) (MIR methods are described in the eAppendix in the Supplement) using matched incidence and mortality data from cancer registries (eTable 6 in the Supplement) and the GBD-estimated health care access and quality index33 (link) as a covariate. These estimated MIRs were then used to convert cancer registry incidence data into inputs for mortality modeling.
Estimating cancer mortality was the second step. The GBD 2019 study used a Cause of Death Ensemble model (CODEm) approach that combined data from vital registration systems, cancer registries, and verbal autopsy reports to estimate mortality across several submodels.34 (link) Covariates provided for potential inclusion in the submodels of the ensemble, such as smoking prevalence or alcohol use, can be found in the eAppendix and eTables 7 and 8 in the Supplement. Ensemble model construction and performance was evaluated through out-of-sample predictive validity tests (eTable 9 in the Supplement). For each cancer, sex-specific CODEm models generated mortality estimates across locations, years, and age groups. These cancer mortality estimates were then scaled to align with the total mortality for all causes of death, which was separately estimated in GBD 2019 (eTable 10 in the Supplement).21 (link) To estimate YLLs, a standard age-specific GBD life expectancy was applied to mortality estimates by age group (eAppendix in the Supplement).20 (link)The GBD cancer incidence and YLD estimation process included 2 additional steps (eFigure 3 in the Supplement), starting with estimating incidence. Incidence was estimated by taking mortality estimates from the second step described previously and dividing by MIR estimates from the first step described previously for each cancer type, sex, location, year, and 5-year age group. Additional information can be found in the eAppendix in the Supplement.
Next, YLDs were estimated by combining prevalence estimates with disability weights associated with various phases of cancer survival. To estimate 10-year cancer prevalence, survival curves estimated from MIRs were combined with GBD-estimated background mortality and applied to incidence estimates. Additional information regarding survival and prevalence estimation can be found in the eAppendix and eFigure 3 in the Supplement. These 10-year prevalence estimates were then partitioned into 4 sequelae according to the expected person-time spent in these 4 phases of cancer survival: (1) diagnosis/treatment, (2) remission, (3) metastatic/disseminated, and (4) terminal (eTable 11 in the Supplement). Each sequela prevalence was multiplied by a sequela-specific disability weight that represented the magnitude of health loss (eTable 12 in the Supplement).20 (link) For 5 cancer types (bladder, breast, colorectal, larynx, and prostate cancer), the total prevalence additionally included lifetime prevalence of procedure-related disability (eg, laryngectomy due to larynx cancer). These procedure-related prevalence estimates were modeled in the Bayesian meta-regression tool DisMod-MR, version 2.1,20 (link) using medical records data on the proportion of patients with cancer who underwent these procedures and the estimated number of 10-year survivors (eAppendix in the Supplement). These procedure-related prevalence estimates were then multiplied by procedure-specific disability weights (eTable 12 in the Supplement). Total cancer-specific YLDs were estimated by summing across these sequelae. Finally, DALYs were estimated as the sum of YLDs and YLLs.20 (link)
Kocarnik J.M., Compton K., Dean F.E., Fu W., Gaw B.L., Harvey J.D., Henrikson H.J., Lu D., Pennini A., Xu R., Ababneh E., Abbasi-Kangevari M., Abbastabar H., Abd-Elsalam S.M., Abdoli A., Abedi A., Abidi H., Abolhassani H., Adedeji I.A., Adnani Q.E., Advani S.M., Afzal M.S., Aghaali M., Ahinkorah B.O., Ahmad S., Ahmad T., Ahmadi A., Ahmadi S., Ahmed Rashid T., Ahmed Salih Y., Akalu G.T., Aklilu A., Akram T., Akunna C.J., Al Hamad H., Alahdab F., Al-Aly Z., Ali S., Alimohamadi Y., Alipour V., Aljunid S.M., Alkhayyat M., Almasi-Hashiani A., Almasri N.A., Al-Maweri S.A., Almustanyir S., Alonso N., Alvis-Guzman N., Amu H., Anbesu E.W., Ancuceanu R., Ansari F., Ansari-Moghaddam A., Antwi M.H., Anvari D., Anyasodor A.E., Aqeel M., Arabloo J., Arab-Zozani M., Aremu O., Ariffin H., Aripov T., Arshad M., Artaman A., Arulappan J., Asemi Z., Asghari Jafarabadi M., Ashraf T., Atorkey P., Aujayeb A., Ausloos M., Awedew A.F., Ayala Quintanilla B.P., Ayenew T., Azab M.A., Azadnajafabad S., Azari Jafari A., Azarian G., Azzam A.Y., Badiye A.D., Bahadory S., Baig A.A., Baker J.L., Balakrishnan S., Banach M., Bärnighausen T.W., Barone-Adesi F., Barra F., Barrow A., Behzadifar M., Belgaumi U.I., Bezabhe W.M., Bezabih Y.M., Bhagat D.S., Bhagavathula A.S., Bhardwaj N., Bhardwaj P., Bhaskar S., Bhattacharyya K., Bhojaraja V.S., Bibi S., Bijani A., Biondi A., Bisignano C., Bjørge T., Bleyer A., Blyuss O., Bolarinwa O.A., Bolla S.R., Braithwaite D., Brar A., Brenner H., Bustamante-Teixeira M.T., Butt N.S., Butt Z.A., Caetano dos Santos F.L., Cao Y., Carreras G., Catalá-López F., Cembranel F., Cerin E., Cernigliaro A., Chakinala R.C., Chattu S.K., Chattu V.K., Chaturvedi P., Chimed-Ochir O., Cho D.Y., Christopher D.J., Chu D.T., Chung M.T., Conde J., Cortés S., Cortesi P.A., Costa V.M., Cunha A.R., Dadras O., Dagnew A.B., Dahlawi S.M., Dai X., Dandona L., Dandona R., Darwesh A.M., das Neves J., De la Hoz F.P., Demis A.B., Denova-Gutiérrez E., Dhamnetiya D., Dhimal M.L., Dhimal M., Dianatinasab M., Diaz D., Djalalinia S., Do H.P., Doaei S., Dorostkar F., dos Santos Figueiredo F.W., Driscoll T.R., Ebrahimi H., Eftekharzadeh S., El Tantawi M., El-Abid H., Elbarazi I., Elhabashy H.R., Elhadi M., El-Jaafary S.I., Eshrati B., Eskandarieh S., Esmaeilzadeh F., Etemadi A., Ezzikouri S., Faisaluddin M., Faraon E.J., Fares J., Farzadfar F., Feroze A.H., Ferrero S., Ferro Desideri L., Filip I., Fischer F., Fisher J.L., Foroutan M., Fukumoto T., Gaal P.A., Gad M.M., Gadanya M.A., Gallus S., Gaspar Fonseca M., Getachew Obsa A., Ghafourifard M., Ghashghaee A., Ghith N., Gholamalizadeh M., Gilani S.A., Ginindza T.G., Gizaw A.T., Glasbey J.C., Golechha M., Goleij P., Gomez R.S., Gopalani S.V., Gorini G., Goudarzi H., Grosso G., Gubari M.I., Guerra M.R., Guha A., Gunasekera D.S., Gupta B., Gupta V.B., Gupta V.K., Gutiérrez R.A., Hafezi-Nejad N., Haider M.R., Haj-Mirzaian A., Halwani R., Hamadeh R.R., Hameed S., Hamidi S., Hanif A., Haque S., Harlianto N.I., Haro J.M., Hasaballah A.I., Hassanipour S., Hay R.J., Hay S.I., Hayat K., Heidari G., Heidari M., Herrera-Serna B.Y., Herteliu C., Hezam K., Holla R., Hossain M.M., Hossain M.B., Hosseini M.S., Hosseini M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Househ M., Hsairi M., Huang J., Hugo F.N., Hussain R., Hussein N.R., Hwang B.F., Iavicoli I., Ibitoye S.E., Ida F., Ikuta K.S., Ilesanmi O.S., Ilic I.M., Ilic M.D., Irham L.M., Islam J.Y., Islam R.M., Islam S.M., Ismail N.E., Isola G., Iwagami M., Jacob L., Jain V., Jakovljevic M.B., Javaheri T., Jayaram S., Jazayeri S.B., Jha R.P., Jonas J.B., Joo T., Joseph N., Joukar F., Jürisson M., Kabir A., Kahrizi D., Kalankesh L.R., Kalhor R., Kaliyadan F., Kalkonde Y., Kamath A., Kameran Al-Salihi N., Kandel H., Kapoor N., Karch A., Kasa A.S., Katikireddi S.V., Kauppila J.H., Kavetskyy T., Kebede S.A., Keshavarz P., Keykhaei M., Khader Y.S., Khalilov R., Khan G., Khan M., Khan M.N., Khan M.A., Khang Y.H., Khater A.M., Khayamzadeh M., Kim G.R., Kim Y.J., Kisa A., Kisa S., Kissimova-Skarbek K., Kopec J.A., Koteeswaran R., Koul P.A., Koulmane Laxminarayana S.L., Koyanagi A., Kucuk Bicer B., Kugbey N., Kumar G.A., Kumar N., Kumar N., Kurmi O.P., Kutluk T., La Vecchia C., Lami F.H., Landires I., Lauriola P., Lee S.W., Lee S.W., Lee W.C., Lee Y.H., Leigh J., Leong E., Li J., Li M.C., Liu X., Loureiro J.A., Lunevicius R., Magdy Abd El Razek M., Majeed A., Makki A., Male S., Malik A.A., Mansournia M.A., Martini S., Masoumi S.Z., Mathur P., McKee M., Mehrotra R., Mendoza W., Menezes R.G., Mengesha E.W., Mesregah M.K., Mestrovic T., Miao Jonasson J., Miazgowski B., Miazgowski T., Michalek I.M., Miller T.R., Mirzaei H., Mirzaei H.R., Misra S., Mithra P., Moghadaszadeh M., Mohammad K.A., Mohammad Y., Mohammadi M., Mohammadi S.M., Mohammadian-Hafshejani A., Mohammed S., Moka N., Mokdad A.H., Molokhia M., Monasta L., Moni M.A., Moosavi M.A., Moradi Y., Moraga P., Morgado-da-Costa J., Morrison S.D., Mosapour A., Mubarik S., Mwanri L., Nagarajan A.J., Nagaraju S.P., Nagata C., Naimzada M.D., Nangia V., Naqvi A.A., Narasimha Swamy S., Ndejjo R., Nduaguba S.O., Negoi I., Negru S.M., Neupane Kandel S., Nguyen C.T., Nguyen H.L., Niazi R.K., Nnaji C.A., Noor N.M., Nuñez-Samudio V., Nzoputam C.I., Oancea B., Ochir C., Odukoya O.O., Ogbo F.A., Olagunju A.T., Olakunde B.O., Omar E., Omar Bali A., Omonisi A.E., Ong S., Onwujekwe O.E., Orru H., Ortega-Altamirano D.V., Otstavnov N., Otstavnov S.S., Owolabi M.O., P A M., Padubidri J.R., Pakshir K., Pana A., Panagiotakos D., Panda-Jonas S., Pardhan S., Park E.C., Park E.K., Pashazadeh Kan F., Patel H.K., Patel J.R., Pati S., Pattanshetty S.M., Paudel U., Pereira D.M., Pereira R.B., Perianayagam A., Pillay J.D., Pirouzpanah S., Pishgar F., Podder I., Postma M.J., Pourjafar H., Prashant A., Preotescu L., Rabiee M., Rabiee N., Radfar A., Radhakrishnan R.A., Radhakrishnan V., Rafiee A., Rahim F., Rahimzadeh S., Rahman M., Rahman M.A., Rahmani A.M., Rajai N., Rajesh A., Rakovac I., Ram P., Ramezanzadeh K., Ranabhat K., Ranasinghe P., Rao C.R., Rao S.J., Rawassizadeh R., Razeghinia M.S., Renzaho A.M., Rezaei N., Rezaei N., Rezapour A., Roberts T.J., Rodriguez J.A., Rohloff P., Romoli M., Ronfani L., Roshandel G., Rwegerera G.M., S M., Sabour S., Saddik B., Saeed U., Sahebkar A., Sahoo H., Salehi S., Salem M.R., Salimzadeh H., Samaei M., Samy A.M., Sanabria J., Sankararaman S., Santric-Milicevic M.M., Sardiwalla Y., Sarveazad A., Sathian B., Sawhney M., Saylan M., Schneider I.J., Sekerija M., Seylani A., Shafaat O., Shaghaghi Z., Shaikh M.A., Shamsoddin E., Shannawaz M., Sharma R., Sheikh A., Sheikhbahaei S., Shetty A., Shetty J.K., Shetty P.H., Shibuya K., Shirkoohi R., Shivakumar K.M., Shivarov V., Siabani S., Siddappa Malleshappa S.K., Silva D.A., Singh J.A., Sintayehu Y., Skryabin V.Y., Skryabina A.A., Soeberg M.J., Sofi-Mahmudi A., Sotoudeh H., Steiropoulos P., Straif K., Subedi R., Sufiyan M.B., Sultan I., Sultana S., Sur D., Szerencsés V., Szócska M., Tabarés-Seisdedos R., Tabuchi T., Tadbiri H., Taherkhani A., Takahashi K., Talaat I.M., Tan K.K., Tat V.Y., Tedla B.A., Tefera Y.G., Tehrani-Banihashemi A., Temsah M.H., Tesfay F.H., Tessema G.A., Thapar R., Thavamani A., Thoguluva Chandrasekar V., Thomas N., Tohidinik H.R., Touvier M., Tovani-Palone M.R., Traini E., Tran B.X., Tran K.B., Tran M.T., Tripathy J.P., Tusa B.S., Ullah I., Ullah S., Umapathi K.K., Unnikrishnan B., Upadhyay E., Vacante M., Vaezi M., Valadan Tahbaz S., Velazquez D.Z., Veroux M., Violante F.S., Vlassov V., Vo B., Volovici V., Vu G.T., Waheed Y., Wamai R.G., Ward P., Wen Y.F., Westerman R., Winkler A.S., Yadav L., Yahyazadeh Jabbari S.H., Yang L., Yaya S., Yazie T.S., Yeshaw Y., Yonemoto N., Younis M.Z., Yousefi Z., Yu C., Yuce D., Yunusa I., Zadnik V., Zare F., Zastrozhin M.S., Zastrozhina A., Zhang J., Zhong C., Zhou L., Zhu C., Ziapour A., Zimmermann I.R., Fitzmaurice C., Murray C.J, & Force L.M. (2021). Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncology, 8(3), 420-444.
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Publication 2021
Age Groups Autopsy Breast Diagnosis Dietary Supplements Disabled Persons Laryngeal Cancer Laryngectomy Larynx Malignant Neoplasms Patients Prostate Cancer sequels Staging, Cancer Survivors Urinary Bladder
2
Evaluating CPIB Across Acquired Disorders
Cited 7 times
Participants representing four medical diagnoses commonly associated with communication disorders were recruited to evaluate the function of the CPIB across different disorder groups. The four groups were multiple sclerosis (MS), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and head and neck cancer (HNCA; oral, oral-pharyngeal or laryngeal cancer, and including those treated with laryngectomy). These groups were chosen for several reasons. First, they are adult-onset conditions, and therefore participants have experienced living as ‘typical’ communicators before the onset of the condition. That provides a perspective of change from which participants can evaluate the impact of the health condition on communicative participation. Because the impact of health conditions on communicative participation has not been directly compared between groups with acquired versus congenital conditions, the decision was made in this study to focus on acquired conditions. Second, the communication disorders associated with these conditions are largely motor speech and voice disorders. In this study, the goal was to target groups who were more likely to retain relatively strong language and cognitive skills because the sole method of data collection was self-report without the presence of a researcher to assist participants with the questionnaires. Finally, these four groups represent different speech and voice disorder characteristics with different trajectories (e.g., stable, slowly degenerative, more rapidly degenerative). This diversity was desired to examine the function of the CPIB items in varying populations.
Baylor C., Yorkston K., Eadie T., Kim J., Chung H, & Amtmann D. (2013). The Communicative Participation Item Bank (CPIB): Item bank calibration and development of a disorder-generic short form. Journal of speech, language, and hearing research : JSLHR, 56(4), 1190-1208.
Publication 2013
Adult Amyotrophic Lateral Sclerosis associated conditions Cancer of Head and Neck Cognition Communicative Disorders Congenital Disorders Diagnosis Laryngeal Cancer Laryngectomy Multiple Sclerosis Oropharynxs Population Group Speech Voice Disorders
3
Estimating Cancer Burden in India
Cited 6 times
A detailed description of methods to estimate cancer mortality, incidence, prevalence, and disability-adjusted life-years (DALYs), and the analytical approaches used in GBD 2016 have been reported elsewhere, and are summarised in the appendix (pp 3–16).1 (link), 25 (link), 26 (link), 27 (link), 28 (link), 29 (link) Briefly, the major data inputs to determine cancer mortality in India included the nationwide Sample Registration System (SRS) cause of death data, the Medically Certified Cause of Death data, and 42 population-based cancer registries (appendix pp 6–8). SRS verbal autopsy cause of death data on 455 460 deaths covering the rural and urban populations of every state of India from 2004 to 2013 were included.4 (link) For states with at least one population-based cancer registry, the incidence data were transformed to mortality by multiplying incidence data with an independently modelled urban or rural mortality-incidence (MI) ratio for the respective states. Cancer registry data were used as the gold-standard against which other data sources (SRS or Medically Certified Cause of Death data) were compared. If the other data sources differed substantially from the registry data, they were excluded.1 (link), 30 (link) Because of limitations associated with the Medically Certified Cause of Death mortality data, these were used only when the cancer type was not captured by the SRS cause of death data. The combined data for cancer mortality were used in a modelling approach (CODEm), where an ensemble of plausible models is selected. The CoDCorrect algorithm was used to adjust cancer subtypes to the parent cause and to adjust the sum of predicted deaths from these models for each type of cancers in an age–sex–state–year group to be consistent with the results from all-cause mortality estimation.
The estimation of cancer incidence was driven by registry data from India. The mortality estimates that were derived from transformation of incidence data using the MI ratios, as noted above, were transformed back to incidence after the CODEm and CoDCorrect model adjustments.1 (link) 10-year cancer prevalence was estimated by modelling survival using the MI ratio as a surrogate for access to cancer care. Incidence cohorts were scaled between a theoretical best and worst case survival using the MI ratio scaling factor. Lifetime prevalence was only estimated for 10 years post incidence1 (link) and for long-term sequelae from procedures (mastectomy, laryngectomy, stoma, incontinence cystectomy, and prostatectomy). Disability for each cancer was estimated by splitting the prevalence into four sequelae: diagnosis and primary treatment, controlled phase, metastatic phase, and terminal phase. Each prevalence sequela was multiplied with specific disability weights to determine years lived with disability (YLDs). We computed years of life lost (YLLs) from the age-specific mortality estimates and a reference life expectancy for that age group. DALYs, a summary measure of total health loss, were computed by adding YLLs and YLDs for each cancer type for location, year, age and sex.1 (link), 28 (link) The appendix provides a list of data inputs used for these estimations (pp 17–29).
A description of estimation of risk factor exposure and its contribution to disease burden in GBD is available elsewhere.29 (link) Briefly, this includes determination of risk exposure and disease outcome pairs based on available evidence and inclusion criteria, assessment of risk exposure from all accessible data sources, and estimation of disease burden attributable to risks based on published relative risks. Estimates of DALYs for specific types of cancers that were attributable to each risk factor were produced by location, age, sex, and year.
GBD uses covariates, which are explanatory variables that have a known association with the outcome of interest, to arrive at the best possible estimate of the outcome of interest when data for the outcome are scarce but data for the covariates are available.25 (link), 26 (link), 27 (link), 28 (link), 29 (link) This approach was part of the estimation process for the findings presented in this report.
The burden of cancers and their variations across the states of India: the Global Burden of Disease Study 1990–2016. (2018). The Lancet. Oncology, 19(10), 1289-1306.
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Publication 2018
Age Groups Autopsy BAD protein, human Cystectomy Diagnosis Disabled Persons Gold Health Risk Assessment Laryngectomy Malignant Neoplasms Mastectomy Parent Prostatectomy sequels Surgical Stoma Urban Population
4
Communication in Community-Dwelling Adults
Cited 6 times
Across all diagnostic groups, the inclusion criteria were adults age 18 or older who had been diagnosed with their medical condition at least three months prior to participation in the study. There were no restrictions on a minimum severity of disorder to qualify for participation, although the recruitment materials stated that the study was intended for individuals who felt that their ability to communicate had been affected by their medical condition. All participants needed to use speech for communication. Individuals could use augmentative or alternative communication (AAC) to supplement speech, but individuals who relied solely on AAC were not included out of concern that the relevance and appropriateness of the items in the CPIB have not yet been evaluated with AAC users. Individuals with laryngectomy could use any speech method. With regards to cognitive or linguistic function, instructions were included that the questionnaires were to be completed via self-report. While participants could receive assistance in the logistical aspects of filling out the questionnaires such as having items read to them or having someone mark their answers, the answers had to be the answers of the person with the communication disorder. If cognitive or language disorders prevented individuals from providing their own answers, the individual was excluded from the study. These issues were addressed in the screening process. There were no restrictions on treatment history although information about treatment history was collected for analysis in future studies. Participants were community-dwelling. Residents of skilled nursing facilities were not included because they are not likely engaged in the same communication situations as community-dwelling adults, and the items in the CPIB address community-based communication situations. The final inclusion criterion was English proficiency because the CPIB has not yet been translated into other languages.
Baylor C., Yorkston K., Eadie T., Kim J., Chung H, & Amtmann D. (2013). The Communicative Participation Item Bank (CPIB): Item bank calibration and development of a disorder-generic short form. Journal of speech, language, and hearing research : JSLHR, 56(4), 1190-1208.
Publication 2013
Adult Cognition Communicative Disorders Dietary Supplements Feelings Language Disorders Laryngectomy Speech
5
Modeling Cancer Burden Estimation
Cited 5 times
In GBD 2019, the initial step in the process of estimating the burden of cancer was modelling cause-specific mortality. Mortality data from multiple sources, including vital registries and verbal autopsies, were extracted. Because of scarce mortality data for some locations and time points, mortality measures were also estimated from the cancer registry incidence data with separately modelled mortality-to-incidence ratios (MIRs). The codes corresponding to cancers in the GBD cause hierarchy were taken from the International Classification of Diseases (ICD)-9 and ICD-10 codebooks and mapped to the GBD cause list for each cancer (appendix 1 p 28). The mortality estimates were then used as inputs for a Cause of Death Ensemble model (CODEm), which predicts single-cause mortality based on the available data and covariates with a causal relationship.11 (link), 12 (link) Additionally, to ensure that all single-cause mortality estimates matched the separately modelled all-cause mortality estimates, CoDCorrect was used to scale single-cause mortality estimates to all-cause mortality estimates.1 (link) The incidence of each cancer was calculated by dividing the cause-specific mortality estimates by the MIRs.
The survival of each cancer was modelled on the basis of MIR estimates for each location, year, sex, and age. The yearly prevalence of the population that did not survive beyond 10 years was divided into four sequelae corresponding to phases of the disease—diagnosis and primary therapy, the controlled phase, the metastatic phase, and the terminal phase—while the yearly prevalence of the population that survived beyond 10 years was only divided into the first and second phases. Disability weights associated with each of these four phases were multiplied by the sequelae prevalence to obtain the years lived with disability (YLDs). For larynx cancer, additional disability due to laryngectomy was also calculated using hospital data to estimate the proportion of the population with larynx cancer that underwent a laryngectomy. The hospital data sources and related ICD codes are described in appendix 1 (p 31). The years of life lost (YLLs) associated with each cancer were calculated by multiplying the number of deaths by age using a standard life expectancy at that age.1 (link) Disability-adjusted life-years (DALYs) were calculated by summing the YLDs and YLLs.1 (link)
Global, regional, and national burden of respiratory tract cancers and associated risk factors from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. (2021). The Lancet. Respiratory Medicine, 9(9), 1030-1049.
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Publication 2021
Autopsy Diagnosis Disabled Persons Laryngeal Cancer Laryngectomy Malignant Neoplasms sequels Therapeutics

Most recents protocols related to «Laryngectomy»

1
Characterization of Autoimmune Thyroid Disease
Thyroid tissue samples were collected from surgeries from AITD patients at the Hospital Universitario de la Princesa and from non-thyroid pathology laryngectomy samples or healthy organ donors at the Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP-HUGTIP) Biobank. Clinical diagnoses were all reviewed by a single experienced endocrinologist based on standard clinical, laboratory, and histological criteria. Serum free thyroxine (FT4), thyroid-stimulating hormone (TSH), and antibodies against thyroglobulin (TG), thyroperoxidase (TPO), and TSH receptor (TSH-R) were determined in all patients at the time of the surgery. Clinical data are shown in Table 1.
This study was approved by the Internal Ethical Review Committee of Hospital Universitario de la Princesa (Committee Register Number: 2796, approval date: 26 May 2016), and written informed consent was obtained from all patients in accordance with the Declaration of Helsinki.
Sacristán-Gómez P., Serrano-Somavilla A., Castro-Espadas L., Sánchez de la Blanca Carrero N., Sampedro-Núñez M., Muñoz-De-Nova J.L., Molina-Jiménez F., Rosell A., Marazuela M, & Martínez-Hernández R. (2023). Evaluation of Epithelial–Mesenchymal Transition Markers in Autoimmune Thyroid Diseases. International Journal of Molecular Sciences, 24(4), 3359.
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Publication 2023
anti-thyroglobulin antibody Clinical Laboratory Services Diagnosis Donor, Organ Endocrinologists Ethical Review Inpatient Iodide Peroxidase Laryngectomy Operative Surgical Procedures Patients Serum Thyroid Gland Thyrotropin Thyrotropin Receptor Thyroxine Tissues
2
Retrospective Study of Laryngectomy
In this retrospective study, we included all patients who underwent TL between 2000 and 2020, at our institution, for a locally advanced (T3 or T4) squamous cell carcinoma of the larynx or hypopharynx, either as a primary therapeutic option or as a salvage procedure after failure of an LP program. The therapeutic strategy was elaborated for each specific patient during a multidisciplinary tumor board (MTB) discussion. The exclusion criteria were as follows: histology other than squamous cell carcinoma, tumor stage T1 or T2 at diagnosis, TL performed for an indication other than progressive cancer, salvage TL performed after a treatment other than a LP protocol (open or transoral partial laryngectomy, radiotherapy alone). LP protocols consisted of either cisplatin-based concurrent CRT or ICT (with PF before 2005 or TPF since 2005) followed by RT in good responders.
Shoushtari S.T., Gal J., Chamorey E., Schiappa R., Dassonville O., Poissonnet G., Aloi D., Barret M., Safta I., Saada E., Sudaka A., Culié D, & Bozec A. (2023). Salvage vs. Primary Total Laryngectomy in Patients with Locally Advanced Laryngeal or Hypopharyngeal Carcinoma: Oncologic Outcomes and Their Predictive Factors. Journal of Clinical Medicine, 12(4), 1305.
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Publication 2023
Cisplatin Diagnosis Hypopharynx Laryngeal Squamous Cell Carcinoma Laryngectomy Malignant Neoplasms Neoplasms Patients Radiotherapy Squamous Cell Carcinoma
3
Laryngeal Cancer Tissue Protein Expression
Thirty-two patients who underwent partial or total laryngectomy for LSCC at the Department of Otolaryngology and Head and Neck Surgery, University Hospital of Split, Split, Croatia, between 1 January 2017 and 31 December 2018, were included in this retrospective study. Patients who had undergone primary oncologic treatment and patients with distant metastases were excluded from the study. Paraffin blocks of laryngeal tumor tissue and adjacent normal mucosa were obtained from the Department of Pathology, Cytology and Forensic Medicine of the same institution, and clinical data were collected from the hospital records. For each patient, the following data of interest were recorded: age, sex, tumor location, type of surgery, tumor size, TNM staging, lymphovascular and perineural invasion and pathohisto-logical grade (Figure 1). Immunofluorescence staining was performed at the Department of Anatomy, Histology and Embryology, University of Split, School of Medicine. For every patient, both malignant and normal laryngeal tissues were discriminated by an experienced pathologist and simultaneous staining was performed. The expression of connexin 37, connexin 40, connexin 45, pannexin-1 and vimentin was analyzed in the tumor samples in comparison to the adjacent healthy tissue by two independent investigators.
Mizdrak I., Mizdrak M., Racetin A., Bošković B., Benzon B., Durdov M.G., Vukojević K, & Filipović N. (2023). Expression of Connexins 37, 40 and 45, Pannexin 1 and Vimentin in Laryngeal Squamous Cell Carcinomas. Genes, 14(2), 446.
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Publication 2023
connexin 37 connexin 40 connexin 45 Cytological Techniques Head Immunofluorescence Laryngeal Neoplasm Laryngectomy Larynx Mucous Membrane Neck Neoplasm Metastasis Neoplasms Neoplasms by Site Operative Surgical Procedures Paraffin Pathologists Patients Tissues Vimentin
4
Nationwide Analysis of Esophageal Cancer Surgeries
This retrospective, cohort study was based on nationwide Taiwanese data obtained from the Taiwan Cancer Registry (TCR), the National Health Insurance (NHI) claims repository, the National Death Registry, and the Registry of Board-certified Specialists and Hospitals. All sources were administered by the Health and Welfare Data Science Center (Taiwanese Ministry of Health and Welfare), a state agency that coordinates collection of hospital administrative data linked with death certificates. We extracted records for all Taiwanese subjects who had undergone elective transthoracic esophagectomy for cancer with gastric tube reconstruction in Taiwanese hospital trusts between 2008 and 2015. Patients with double malignancies or missing information were excluded, as were patients who received complex esophageal resections (e.g., esophagectomy combined with laryngectomy) or reconstruction methods other than gastric tube reconstruction. Figure 1 shows the flow of participants through the study. Patients were categorized according to the day of surgery (weekday group: surgical procedures starting Monday through Friday; weekend group: surgical procedures starting on Saturday or Sunday). Local institutional review board approval (Chang Gung Memorial Hospital IRB Reference Number: 201801603B0) was obtained to process and analyze all records. The need for patient consent was waived, because all study data were deidentified.

Flow of patients through the study

Yang T.Y., Wen Y.W, & Chao Y.K. (2023). Impact of Weekend Effect on Short- and Long-Term Survival of Patients Undergoing Esophagectomy for Cancer: A Population-Based, Inverse Probability of Treatment-Weighted Analysis. Annals of Surgical Oncology, 30(6), 3790-3798.
Publication 2023
Esophagectomy Laryngectomy Malignant Neoplasms National Health Insurance Operative Surgical Procedures Patients Reconstructive Surgical Procedures Specialists Stomach Surgery, Day
5
Isolation of Primary Head and Neck Cells
Patient samples (n=4) were obtained from the Loma Linda University School of Medicine, Department of Otolaryngology and Head/Neck Surgery through the Loma Linda University Tissue Biorepository. Patients were all males with ages ranging from 65 to 77 years and the tissues were obtained following laryngectomy and glossectomy. The freshly resected tissues were kept on ice until ready for processing. Samples were transferred to petri dishes and washed with PBS-2X Gentamicin 3 times. Samples were minced using a sterile razor blade until they appeared as puree. The puree was passed over a 70 µm strainer using a plunger from a 3 mL syringe. Plain DMEM was used to wash the cells. The cells were then centrifuged at 1500 rpm for 10 mins. If pellet was red, red blood cells were removed using Ficoll. Otherwise, cells were counted using an automated cell counter and resuspended in three parts Ham’s F12, one part DMEM (Fisher Scientific) supplemented with 5% FBS (Omega Scientific), 10uM insulin, 0.4uM hydrocortisone, 2ug/ml isoprenaline, 24ug/ml adenine (chemicals from Sigma-Aldrich), 100U/ml penicillin, 10ug/ml streptomycin (Fisher Scientific). 5-10 uM Y27632 (BioGems) was added to establish growth in vitro (26 (link)). Cells were plated at 2 X 106 in 6 well plates. Cells were monitored over time for development of clones that were then used to establish primary cultures.
Chitsike L., Bertucci A., Vazquez M., Lee S., Unternaehrer J.J, & Duerksen-Hughes P.J. (2023). GA-OH enhances the cytotoxicity of photon and proton radiation in HPV+ HNSCC cells. Frontiers in Oncology, 13, 1070485.
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Publication 2023
Adenine Cells Clone Cells Erythrocytes Ficoll Gentamicin Glossectomy Head Hydrocortisone Hyperostosis, Diffuse Idiopathic Skeletal Insulin Isoproterenol Laryngectomy Loma Males Neck Operative Surgical Procedures Patients Penicillins Sterility, Reproductive Streptomycin Syringes Tissues Y 27632

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