diagnostic algorithm in combined surgical pathology in patients with diseases of endocrine system

diagnostic algorithm in combined surgical pathology in patients with diseases of endocrine system

;S.M. Zavgorodniy;M.A. Kubrak;A.I. Rylov;M.B. Danilyk
Macromolecular rapid communications 2017 Vol. 13 pp. 591-595
41
zavgorodniy2017mnarodnijdiagnostic

Abstract

Background. Every 2 years, the incidence of endocrine pathology increased by 23–30 %. During the diagnostic search, 25–30 % of patients with endocrine disorders appear to have 2 to 4 combined surgical diseases. The number of simultaneous surgical interventions in this group of patients remains at the level of 1.5–6 %. The purpose of the study was to develop a diagnostic algorithm for detection of combined surgical diseases in patients with disorders of the endocrine system. Materials and methods. We performed the analysis of 764 case records in patients with endocrine disorders who underwent surgery in the period from 2012 to 2016 on the premises of MI “City Clinical Hospital of Emergency Medical Care” and the Zaporizhzhia Regional Clinical Hospital. Results. The analysis of treatment outcomes in this group of patients showed that the number of simultaneous operations in patients with combined surgical pathology was 5.5 %. Conclusions. A small number of simultaneous surgical interventions in patients with endocrine pathology is determined by the low detection rate of combined surgical diseases, which is associated with inadequate examination of patients in the preoperative period. For timely and complete diagnosis of combined surgical pathology in patients with diseases of the endocrine system, it is necessary to extend the standard protocols of patient’s examination and to implement a wide application of additional research methods. The suggested algorithm for examination of patients with disorders of the endocrine system will allow you to diagnose concomitant surgical pathology.

Citation

ID: 243052
Ref Key: zavgorodniy2017mnarodnijdiagnostic
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
243052
Unique Identifier:
10.22141/2224-0721.13.8.2017.119276
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet