Abdominal wound dehiscence in adults: development and validation of a risk model.
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ABSTRACT:
SUBMITTER: van Ramshorst GH
PROVIDER: S-EPMC2795859 | biostudies-other | 2010 Jan
REPOSITORIES: biostudies-other
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Project description:INTRODUCTION: Uterine scar dehiscence can complicate caesarean section with complications like post partum hemorrhage, endomyometritis, localized/generalized peritonitis, and sepsis. PRESENTATION OF CASE: Our patient had abdominal wound infection after LSCS surgery and features of sepsis. The wound infection was actually the presentation of a uterine scar dehiscence and localized peritonitis. DISCUSSION: Incidence of uterine scar dehiscence is around 0.6%. Presentation can be post partum hemorrhage, endomyometritis, and generalized/localized peritonitis. Peritonitis caused by uterine incisional necrosis must be dealt surgically. A high index of suspicion with appropriate investigations can highlight such problems for early treatment and cure with least morbidity especially related to further pregnancies. CONCLUSION: Uterine scar dehiscence with infection requires high index of suspicion as rare cause for post partum localized/generalized peritonitis with sepsis. Severe abdominal wound infection after caesarean section may be associated with uterine wound dehiscence, which poses a grave risk to the mother in a future pregnancy.
| S-EPMC4392185 | biostudies-other
Project description:Background Abdominal wound dehiscence (AWD) is the separation of different layers of an abdominal wound before complete healing has taken place. It is a major cause of postoperative morbidity and mortality in sub-Saharan Africa including Ethiopia, and little is known about its prevalence and related factors in the study area. Objectives The aim of this study is to assess the magnitude of abdominal wound dehiscence and related factors on patients operated at St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia. Methods A hospital-based retrospective review of the chart was carried out by using the data covering three years (September 2014–September 2017) period. Data were collected from hospital medical records of sampled patients such as operation room logbooks and individual patient medical records. The collected data were checked for consistency, coded, and entered into SPSS version 20 for data processing and analysis. Descriptive analysis was conducted, and tables and graphs and summary statistics were used to depict data. Results A total of 41 patients developed abdominal wound dehiscence from among 4137 patients who underwent abdominal laparotomy in the hospital. Among the patients, 51.2% were in the age range of 41 and above with mean age 29.8 (SD?=?1.21) and 70.7% of them were male. Abdominal wound dehiscence was more common in emergency patients (90%) and vertical incision was the most common type of incision. Over half (58.5%) of the wound dehiscence occurred within 6–10 postoperative days. The majority (95.2%) of dehisced patients underwent relaparotomy for the management of the wound dehiscence, and 48.8% of them were treated with tension suture during the second operation of abdominal closure. Four of the patients (9.7%) died after the management of the second operation. Conclusion The current study revealed that the overall magnitude of abdominal wound dehiscence in the study area was 0.99%. Most of the dehiscence has occurred in male patients, and older age groups were highly affected than the younger ones. Emergency admission is the most common form of admission identified in the study, and this signifies appropriate preoperative preparation of patients for an optimal outcome. However, regarding the management outcome, 9.8% of patients died in our study within the institution after the second operation which is the high mortality rate.
| S-EPMC7060443 | biostudies-literature
Project description:ObjectiveThis research aims to construct and authenticate a comprehensive predictive model for all-cause mortality, based on a multifaceted array of risk factors.MethodsThe derivation cohort for this study was the Chinese Longitudinal Healthy Longevity Survey (CLHLS), while the Healthy Ageing and Biomarkers Cohort Study (HABCS) and the China Health and Retirement Longitudinal Study (CHARLS) were used as validation cohorts. Risk factors were filtered using lasso regression, and predictive factors were determined using net reclassification improvement. Cox proportional hazards models were employed to establish the mortality risk prediction equations, and the model's fit was evaluated using a discrimination concordance index (C-index). To evaluate the internal consistency of discrimination and calibration, a 10x10 cross-validation technique was employed. Calibration plots were generated to compare predicted probabilities with observed probabilities. The prediction ability of the equations was demonstrated using nomogram.ResultsThe CLHLS (mean age 88.08, n = 37074) recorded 28158 deaths (179683 person-years) throughout the course of an 8-20 year follow-up period. Additionally, there were 1384 deaths in the HABCS (mean age 86.74, n = 2552), and 1221 deaths in the CHARLS (mean age 72.48, n = 4794). The final all-cause mortality model incorporated demographic characteristics like age, sex, and current marital status, as well as functional status indicators including cognitive function and activities of daily living. Additionally, lifestyle factors like past smoking condition and leisure activities including housework, television viewing or radio listening, and gardening work were included. The C-index for the derivation cohort was 0.728 (95% CI: 0.724-0.732), while the external validation results for the CHARS and HABCS cohorts were 0.761 (95% CI: 0.749-0.773) and 0.713 (95% CI: 0.697-0.729), respectively.ConclusionThis study introduces a reliable, validated, and acceptable mortality risk predictor for older adults in China. These predictive factors have potential applications in public health policy and clinical practice.
| S-EPMC10825771 | biostudies-literature
Project description:To investigate the incidence, causes, occurrence time, and range of wound and outcomes of wound dehiscence in patients treated by penetrating keratoplasty (PK) or lamellar keratoplasty (LK).We retrospectively reviewed medical records of keratoplasty in Shandong Eye Hospital from January 2006 to June 2017. Thirty-one eyes of 30 patients had sustained wound dehiscence (WD) after surgical treatment. The surgical type, causes, occurrence time, extent of the wound, treatment, and outcomes were recorded.The study population consisted of 26 men and 4 women. The mean age at the occurrence of WD was 44.6 years old (range: 12-78 years), and the mean time from keratoplasty to WD was 45.9 months (range: 1-204 months). WD occurred in 23 eyes (23/1385, 1.66%) after PK and 8 eyes (8/1632, 0.49%) after LK (p < 0.05). Twenty-seven eyes (27/31, 87.0%) had trauma-induced dehiscence. The mean range of dehiscence was 5.5 o'clock. The vision ranged from 20/50 to light perception after wound suture. The eyes receiving LK had fewer serious complications than PK.Compared with LK, PK seems to be more prone to result in wound dehiscence. The WD after LK may be less severe. The visual acuity after treatment of WD can be worse in the eyes with PK than LK.
| S-EPMC6079506 | biostudies-literature
Project description:ImportancePostpancreatectomy hemorrhage (PPH) due to postoperative pancreatic fistula (POPF) is a life-threatening complication after pancreatoduodenectomy. However, there is no prediction tool for early identification of patients at high risk of late PPH.ObjectiveTo develop and validate a prediction model for PPH.Design, setting, and participantsThis retrospective prognostic study included consecutive patients with clinically relevant POPF who underwent pancreatoduodenectomy from January 1, 2009, to May 20, 2023, at the University Hospital Mannheim (derivation cohort), and from January 1, 2012, to May 31, 2022, at the University Hospital Dresden (validation cohort). Data analysis was performed from May 30 to July 29, 2023.ExposureClinical and radiologic features of PPH.Main outcomes and measuresAccuracy of a predictive risk score of PPH. A multivariate prediction model-the hemorrhage risk score (HRS)-was established in the derivation cohort (n = 139) and validated in the validation cohort (n = 154).ResultsA total of 293 patients (187 [64%] men; median age, 69 [IQR, 60-76] years) were included. The HRS comprised 4 variables with associations: sentinel bleeding (odds ratio [OR], 35.10; 95% CI, 5.58-221.00; P < .001), drain fluid culture positive for Candida species (OR, 14.40; 95% CI, 2.24-92.20; P < .001), and radiologic proof of rim enhancement of (OR, 12.00; 95% CI, 2.08-69.50; P = .006) or gas within (OR, 12.10; 95% CI, 2.22-65.50; P = .004) a peripancreatic fluid collection. Two risk categories were identified with patients at low risk (0-1 points) and high risk (≥2 points) to develop PPH. Patients with PPH were predicted accurately in the derivation cohort (C index, 0.97) and validation cohort (C index 0.83). The need for more invasive PPH management (74% vs 34%; P < .001) and severe complications (49% vs 23%; P < .001) were more frequent in high-risk patients compared with low-risk patients.Conclusions and relevanceIn this retrospective prognostic study, a robust prediction model for PPH was developed and validated. This tool may facilitate early identification of patients at high risk for PPH.
| S-EPMC10701614 | biostudies-literature
Project description:Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.
| S-EPMC2854402 | biostudies-other
Project description:AimTo develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes.MethodsThere were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance.ResultsWe developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively.ConclusionsWe developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.
| S-EPMC10107751 | biostudies-literature
Project description:BackgroundSkeletal muscle is a large and clinically relevant body component that has been difficult and impractical to quantify outside of specialized facilities. Advances in smartphone technology now provide the opportunity to quantify multiple body surface dimensions such as circumferences, lengths, surface areas, and volumes.ObjectivesThis study aimed to test the hypothesis that anthropometric body measurements acquired with a smartphone application can be used to accurately estimate an adult's level of muscularity.MethodsAppendicular lean mass (ALM) measured by DXA served as the reference for muscularity in a sample of 322 adults. Participants also had digital anthropometric dimensions (circumferences, lengths, and regional and total body surface areas and volumes) quantified with a 20-camera 3D imaging system. Least absolute shrinkage and selection operator (LASSO) regression procedures were used to develop the ALM prediction equations in a portion of the sample, and these models were tested in the remainder of the sample. Then, the accuracy of the prediction models was cross-validated in a second independent sample of 53 adults who underwent ALM estimation by DXA and the same digital anthropometric estimates acquired with a smartphone application.ResultsLASSO models included multiple significant demographic and 3D digital anthropometric predictor variables. Evaluation of the models in the testing sample indicated respective RMSEs in women and men of 1.56 kg and 1.53 kg and R2's of 0.74 and 0.90, respectively. Cross-validation of the LASSO models in the smartphone application group yielded RMSEs in women and men of 1.78 kg and 1.50 kg and R2's of 0.79 and 0.95; no significant differences or bias between measured and predicted ALM values were observed.ConclusionsSmartphone image capture capabilities combined with device software applications can now provide accurate renditions of the adult muscularity phenotype outside of specialized laboratory facilities. Am J Clin Nutr 2023;x:xx. This trial was registered at clinicaltrials.gov as NCT03637855 (https://clinicaltrials.gov/ct2/show/NCT03637855), NCT05217524 (https://clinicaltrials.gov/ct2/show/NCT05217524), and NCT03771417 (https://clinicaltrials.gov/ct2/show/NCT03771417).
| S-EPMC10315403 | biostudies-literature
Project description:ObjectiveThis study sought to develop and validate a 6-year risk prediction model in older adults with cognitive frailty (CF).MethodsIn the secondary analysis of Chinese Longitudinal Healthy Longevity Survey (CLHLS), participants from the 2011-2018 cohort were included to develop the prediction model. The CF was assessed by the Chinese version of Mini-Mental State Exam (CMMSE) and the modified Fried criteria. The stepwise regression was used to select predictors, and the logistic regression analysis was conducted to construct the model. The model was externally validated using the temporal validation method via the 2005-2011 cohort. The discrimination was measured by the area under the curve (AUC), and the calibration was measured by the calibration plot. A nomogram was conducted to vividly present the prediction model.ResultsThe development dataset included 2420 participants aged 60 years or above, and 243 participants suffered from CF during a median follow-up period of 6.91 years (interquartile range 5.47-7.10 years). Six predictors, namely, age, sex, residence, body mass index (BMI), exercise, and physical disability, were finally used to develop the model. The model performed well with the AUC of 0.830 and 0.840 in the development and external validation datasets, respectively.ConclusionThe study could provide a practical tool to identify older adults with a high risk of CF early. Furthermore, targeting modifiable factors could prevent about half of the new-onset CF during a 6-year follow-up.
| S-EPMC10822804 | biostudies-literature
Project description:Background Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year. Methods and Results We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all-cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01-1.05), better physical health (OR, 0.98; 95% CI, 0.97-0.99), in-hospital complication of heart failure (OR, 1.44; 95% CI, 0.99-2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96-1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00-1.52), female sex (OR, 1.31; 95% CI, 1.05-1.65), low income (OR, 1.13; 95% CI, 0.89-1.42), prior AMI (OR, 1.47; 95% CI, 1.15-1.87), in-hospital length of stay (OR, 1.13; 95% CI, 1.04-1.23), and being employed (OR, 0.88; 95% CI, 0.69-1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts). Conclusions Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.
| S-EPMC8649501 | biostudies-literature