scholarly journals Risk factors that predict major amputations and amputation time intervals for hospitalised diabetic patients with foot complications

2021 ◽  
Author(s):  
Yu‐Yu Chou ◽  
Chin‐Chun Hou ◽  
Chien‐Wei Wu ◽  
Dun‐Wei Huang ◽  
Sheng‐Lin Tsai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Patients ◽  
Time Intervals ◽  
Foot Complications

scholarly journals Evaluation of a screening and prevention programme for diabetic foot complications

2001 ◽  
Vol 25 (2) ◽  
pp. 132-138 ◽  
Author(s):  
J. W. G. Meijer ◽  
T. P. Links ◽  
A. J. Smit ◽  
J. W. Groothoff ◽  
W. H. Eisma
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Outpatient Clinic ◽  
Diabetic Foot ◽  
Preventive Measures ◽  
University Hospital ◽  
Prevention Programme ◽  
Diabetic Patients ◽  
Foot Complications ◽  
Screening And Prevention

Foot complications in diabetes can be decreased by preventive measures. The authors evaluated the current diabetic foot screening and prevention programme of the diabetes outpatient clinic of their university hospital, by assessing the presence of risk factors for the development of foot disorders and the preventive measures taken. Fifty (50) diabetic patients not known to have foot complications were selected at random. Risk factors and preventive measures were inventarised with the Coleman risk-categorization system and the Preventive Measures Scale, respectively. Sixty per cent (60%) of the patients were at risk of developing diabetic foot complications. The preventive measures were low in these patients. Patient knowledge was insufficient and behaviour even worse. Basal preventive shoe adaptations were absent in most patients at risk. No relation between risk category and the preventional status was found. Cross-sectional examination at a university outpatient clinic showed serious risk profiles for foot complications, which were not balanced by the application of generally accepted preventive measures. At the outpatient clinic, screening should be optimised.

Assessment of Risk Factors for Severe Coronavirus Disease 2019 among Iranian Patients

2020 ◽  
Keyword(s):  
Risk Factors ◽  
Immune System ◽  
Medical Records ◽  
Critical Role ◽  
Diabetic Patients ◽  
Medication History ◽  
Epidemiological Features ◽  
Hookah Smoking ◽  
And Behavior ◽  
Iranian Patients

Background: The outbreak of Coronavirus Disease 2019 (COVID-19) has led to a major concern for those who are more vulnerable to infections. Objectives: This study aimed to evaluate the most important risk factors for severe COVID-19 pneumonia. Methods: This retrospective study included information on clinical and epidemiological features of 105 patients with severe COVID-19 pneumonia hospitalized in Tajrish Hospital, Tehran, Iran. Initially, the medical records of the patients were investigated, and an interview was conducted based on a pre-prepared checklist to seek information about symptoms, past medical history, medication history, and behavior before hospitalization. Results: Out of 105 participants, 76 (72.5%) cases were male, and 54 (51.4%) patients were older than 54 years old. The majority of the patients (n=18; 17.1%) had both hypertension and diabetes (n=12; 11.4%). Metformin (n=36; 34.3%) was the most used medication amongst the studied patient. In addition, 24 (22.9%) patients were recreational hookah smokers, and the majority (75%) of them were under the age of 46 years old. Eventually, 19 patients were excluded from the study, of whom 11 individuals had diabetes, and 10 cases were using metformin. Conclusion: Apparently, hookah smoking played a critical role in the spread of COVID-19 in Iran and has made younger people more susceptible. In addition to older age, the immunosuppressive effects of Metformin seem to make diabetic patients with an impaired immune system more vulnerable to severe COVID-19 pneumonia. More studies on the immune system of vulnerable individuals by identifying their differences can help to protect them.

RISK FACTORS FOR EARLY DEVELOPMENT OF POLYNEUROPATHY AMONG TYPE II DIABETIC PATIENTS.

2017 ◽  
Vol 5 (10) ◽  
pp. 1197-1202
Author(s):  
Mahfouz HM ◽  
◽  
HashemA H ◽  
YasserHamed Mostafa ◽  
◽  
...  
Keyword(s):  
Risk Factors ◽  
Early Development ◽  
Diabetic Patients ◽  
Type Ii

scholarly journals When Uncontrolled Diabetes Mellitus and Severe COVID-19 Converge: The Perfect Storm for Mucormycosis

2021 ◽  
Vol 7 (4) ◽  
pp. 298
Author(s):  
Teny M. John ◽  
Ceena N. Jacob ◽  
Dimitrios P. Kontoyiannis
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Clinical Features ◽  
The Other ◽  
Diabetic Patients ◽  
Epidemiological Factors ◽  
Uncontrolled Diabetes ◽  
Diagnostic Certainty ◽  
Patients With Diabetes ◽  
Perfect Storm

Mucormycosis (MCR) has been increasingly described in patients with coronavirus disease 2019 (COVID-19) but the epidemiological factors, presentation, diagnostic certainty, and outcome of such patients are not well described. We review the published COVID-19-associated mucormycosis (CAMCR) cases (total 41) to identify risk factors, clinical features, and outcomes. CAMCR was typically seen in patients with diabetes mellitus (DM) (94%) especially the ones with poorly controlled DM (67%) and severe or critical COVID-19 (95%). Its presentation was typical of MCR seen in diabetic patients (mostly rhino-orbital and rhino-orbital-cerebral presentation). In sharp contrast to reported COVID-associated aspergillosis (CAPA) cases, nearly all CAMCR infections were proven (93%). Treating physicians should have a high suspicion for CAMCR in patients with uncontrolled diabetes mellitus and severe COVID-19 presenting with rhino-orbital or rhino-cerebral syndromes. CAMR is the convergence of two storms, one of DM and the other of COVID-19.

scholarly journals Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors

2021 ◽  
Vol 11 (5) ◽  
pp. 328
Author(s):  
Michael Leutner ◽  
Nils Haug ◽  
Luise Bellach ◽  
Elma Dervic ◽  
Alexander Kautzky ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Disease ◽  
Diabetic Patients ◽  
Complication Risk ◽  
Increased Risk ◽  
Icd 10 ◽  
Healthy Control ◽  
Design And Methods ◽  
The Relationship ◽  
Observation Period

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.

scholarly journals Mortality and Conversion Rate to Below Knee or Above Knee Amputation Following Transmetatarsal Amputation

2020 ◽  
Vol 5 (4) ◽  
pp. 2473011420S0011
Author(s):  
D. Anthony Barcel ◽  
Susan M. Odum ◽  
Taylor Rowe ◽  
Jefferson B. Sabatini ◽  
Samuel E. Ford ◽  
...  
Keyword(s):  
Risk Factors ◽  
Conversion Rate ◽  
Significant Risk ◽  
High Rate ◽  
Administrative Database ◽  
Categorical Variables ◽  
Diabetic Patients ◽  
Knee Amputation ◽  
History Of ◽  
Transmetatarsal Amputation

Category: Midfoot/Forefoot; Diabetes; Other Introduction/Purpose: Non-traumatic lower extremity amputations (LEA), especially those performed in dysvascular and diabetic patients, are known to have poor long-term prognosis. Perioperative mortality has been reported at between 4 and 10%, and the 1 and 5 year mortality rates range between 22-33% and 39-69%, respectively. While poor outcomes in these patients have been described, there is no consensus as to the predictors of mortality. The purpose of the study is to determine the percentage of patients who had a complication following transmetatarsal amputation (TMA) and identify associated risk factors for complications and mortality. Methods: We queried our institution’s administrative database to identify 247 TMA procedures performed in 229 patients between January, 2002 and December, 2016. Electronic health records were reviewed to document complications defined as reoperation, amputation and mortality. Mortality was also verified using the National Death Index. Additionally, we recorded risk factors including diabetes, A1c level, end stage renal disease (ESRD), cardiovascular disease (CVD), peripheral vascular disease (PVD), history of revascularization, contralateral amputation, and neuropathy. The majority of the study patients were males (157, 69%) and the average age was 57 years (range 24-91). The median BMI was 28 (range 16-58) and 29% of the study patients were obese with a BMI ≥ 30. Fishers Exact tests were used to compare categorical variables. Kruskal-Wallis and Independent T-tests were used to compare numeric data. All data were analyzed using SAS/STAT software version 9.4 (Carey, NC) and a 0.05 level of significance was defined apriori. Results: The conversion rate to below (BKA) or above knee amputation (AKA) was 26% (64 of 247). Males (p=.0274), diabetics (p=.0139), patients in ESRD (p=.019), and patients with a history of CVD (p=.0247) or perioperative revascularization (p=.022) were more likely to undergo further amputation following an index TMA. BMI was significantly higher in patients requiring BKA/AKA (p=.0305). There were no significant differences in age (p=.2723) or A1c levels (p=.4219). The overall mortality rate was 35% (84 of 229). Diabetes (p=.0272), ESRD (p=.0031), history of CVD (p<.0001) or PVD (p=.0179) were all significantly associated with mortality. Patients who died were significantly older (p=.0006) and had significantly higher A1c levels (p=.0373). BMI was not significantly associated with mortality. Twenty-two patients who had 23 further amputations subsequently died. Conclusion: In our series of patients undergoing TMA, 26% underwent further amputation and 35% of patients died. Conversion rate to BKA or AKA occurred at a high rate regardless of preoperative revascularization or the use of tendo-achilles or gastrocnemius lengthening procedures. Male sex, diabetes, ESRD, history of CVD or revascularization are significant risk factors for further amputation. ESRD, diabetes, history of CVD or PVD, older age and higher A1c levels are significant risk factors for mortality. These data provide useful insight into risk factors to be emphasized when counseling patients and their families to establish realistic postoperative expectations.

scholarly journals Fermented pork fat (Sa-um) and lifestyle risk factors as potential indicators for type 2 diabetes among the Mizo population, Northeast India

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Freda Lalrohlui ◽  
Souvik Ghatak ◽  
John Zohmingthanga ◽  
Vanlal Hruaii ◽  
Nachimuthu Senthil Kumar
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Cox Regression ◽  
Control Group ◽  
Diabetic Patients ◽  
Lifestyle Habits ◽  
Pork Fat

AbstractOver the last few decades, Mizoram has shown an increase in cases of type 2 diabetes mellitus; however, no in-depth scientific records are available to understand the occurrence of the disease. In this study, 500 patients and 500 healthy controls were recruited to understand the possible influence of their dietary and lifestyle habits in relation with type 2 diabetes mellitus. A multivariate analysis using Cox regression was carried out to find the influence of dietary and lifestyle factors, and an unpaired t test was performed to find the difference in the levels of biochemical tests. Out of 500 diabetic patients, 261 (52.3%) were males and 239 (47.7%) were females, and among the control group, 238 (47.7%) were males and 262 (52.3%) were females. Fermented pork fat, Sa-um (odds ratio (OR) 18.98), was observed to be a potential risk factor along with tuibur (OR 0.1243) for both males and females. Creatinine level was found to be differentially regulated between the male and female diabetic patients. This is the first report of fermented pork fat and tobacco (in a water form) to be the risk factors for diabetes. The unique traditional foods like Sa-um and local lifestyle habits like tuibur of the Mizo population may trigger the risk for the prevalence of the disease, and this may serve as a model to study other populations with similar traditional practices.

The prevalence and risk factors associated with dyslipidemia in type 2 diabetic patients in the autonomous Region of Cantabria

2020 ◽  
Vol 67 (2) ◽  
pp. 102-112
Author(s):  
Alcibíades Segundo Díaz Vera ◽  
José Abellán Alemán ◽  
Antonio Segura Fragoso ◽  
Juan Pablo Martínez de Esteban ◽  
Francisco Javier Lameiro Couso ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Patients ◽  
Type 2 Diabetic Patients ◽  
Autonomous Region ◽  
Factors Associated ◽  
Type 2 Diabetic
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