scholarly journals Factors Defining the Development of Severe Illness in Patients with COVID-19: A Single-Center, Retrospective, Observational Study

2020 ◽  
Author(s):  
Xiong Yibai ◽  
Tian Yaxin ◽  
Liu Bin ◽  
Ruan Lianguo ◽  
Lu Cheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Lactate Dehydrogenase ◽  
Critical Illness ◽  
Observational Study ◽  
Risk Score ◽  
Severe Illness ◽  
Single Center ◽  
Training Set ◽  
Validation Set

Abstract Objective Early triage of patients with coronavirus disease 2019 (COVID-19) is pivotal in managing the disease. However, data on the risk factors for the development of severe disease remains scant. Here, we report a clinical risk score system for severe illness and highlight possible protective factors, which might inform proper treatment strategies.Methods We conducted a retrospective, single-center, observational study at the JinYinTan Hospital from January 24,2020 to March 31, 2020. We evaluated the demographic, clinical, and laboratory data and performed a 3-fold cross-validation to split the data into training set and validation set. We then screened the prognostic factors for severe illness using the Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression, and finally conducted a risk score to estimate the probability of critical illness in the training set. Data from the validation set were used to validate the score. Furthermore, the clinical factors of those patients who recovered were compared with those who did not recover from the rapidly worsened illness. We then employed logistic regression tools to delineate the possible protective factors.Results A total of 302 patients were included. From 47 potential risk factors, 6 variables were measured as the risk score: sex(female) (OR, 0.372; 95%CI, 0.211-0.655), Chest Computed Tomography abnormality (OR, 1.90; 95%CI, 1.36-2.66), neutrophil value (OR, 1.33; 95%CI, 1.18-1.50), neutrophil to lymphocyte ratio (OR, 1.23; 95%CI, 1.14-1.34), lactate dehydrogenase (OR, 1.01; 95%CI, 1.006-1.012), albumin (OR, 0.77; 95%CI, 0.71-0.84). The mean AUC of development cohort was 0.82 (95% CI, 0.81-0.92) and the AUC of validation cohort was 0.894 (95% CI, 0.78-0.95). Our comparison data from patients who rapidly worsened but recovered with those who did not showed that 4 variables were predictive factors: Prealbumin (OR, 1.028; 95%CI, 1.010-1.057), percentage of lymphocytes (OR, 1.213; 95%CI, 1.062-1.385), lactate dehydrogenase (OR, 0.984; 95%CI, 0.973-0.996), Prothrombin ativity (OR, 1.065; 95%CI, 1.018-1.115).Conclusion and Relevance In this study, we developed a predictive risk score and highlight 4 factors that might predict recovery from suddenly worsened illness. This report may help define the potential of developing critical illness and recovery prospects in patients with rapidly worsened condition.

scholarly journals Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenhui Zhong ◽  
Feng Zhang ◽  
Kaijun Huang ◽  
Yiping Zou ◽  
Yubin Liu
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Liver Failure ◽  
Roc Curves ◽  
Postoperative Liver Failure ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Independent Risk Factors ◽  
Validation Set

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis ( P < 0.026 , OR = 2.296, 95% confidence interval (CI) 1.1.02–4.786), major hepatectomy ( P = 0.031 , OR = 2.211, 95% CI 1.077–4.542), ascites ( P = 0.014 , OR = 3.588, 95% 1.299–9.913), intraoperative blood loss ( P < 0.001 , OR = 4.683, 95% CI 2.281–9.616), PALBI score >−2.53 (, OR = 3.609, 95% CI 1.486–8.764), and FIB-4 score ≥1.45 ( P < 0.001 , OR = 5.267, 95% CI 2.077–13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.

scholarly journals CD8+ T cells predicted the conversion of common covid-19 to severe

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li Liu ◽  
Zhiyong Chen ◽  
Yingrong Du ◽  
Jianpeng Gao ◽  
Junyi Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
T Cells ◽  
Logistic Regression ◽  
Prediction Model ◽  
Roc Curve ◽  
Multiple Logistic Regression Analysis ◽  
Laboratory Data ◽  
Training Set ◽  
Area Under Roc Curve ◽  
Validation Set

AbstractTo evaluate the predictive effect of T-lymphoid subsets on the conversion of common covid-19 to severe. The laboratory data were collected retrospectively from common covid-19 patients in the First People's Hospital of Zaoyang, Hubei Province, China and the Third People's Hospital of Kunming, Yunnan Province, China, between January 20, 2020 and March 15, 2020 and divided into training set and validation set. Univariate and multivariate logistic regression was performed to investigate the risk factors for the conversion of common covid-19 to severe in the training set, the prediction model was established and verified externally in the validation set. 60 (14.71%) of 408 patients with common covid-19 became severe in 6–10 days after diagnosis. Univariate and multiple logistic regression analysis revealed that lactate (P = 0.042, OR = 1097.983, 95% CI 1.303, 924,798.262) and CD8+ T cells (P = 0.010, OR = 0.903, 95% CI 0.835, 0.975) were independent risk factors for general type patients to turn to severe type. The area under ROC curve of lactate and CD8+ T cells was 0.754 (0.581, 0.928) and 0.842 (0.713, 0.970), respectively. The actual observation value was highly consistent with the prediction model value in curve fitting. The established prediction model was verified in 78 COVID-19 patients in the verification set, the area under the ROC curve was 0.906 (0.861, 0.981), and the calibration curve was consistent. CD8+ T cells, as an independent risk factor, could predict the transition from common covid-19 to severe.

scholarly journals POS0875 MYOSITIS-SPECIFIC ANTIBODIES IN A RETROSPECTIVE SINGLE-CENTER OBSERVATIONAL STUDY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 693.2-694
Author(s):  
J. Álvarez Troncoso ◽  
S. Carrasco Molina ◽  
J. Valdivieso ◽  
P. Nozal ◽  
Á. Robles Marhuenda ◽  
...  
Keyword(s):  
Risk Factors ◽  
Observational Study ◽  
Interstitial Pneumonia ◽  
Usual Interstitial Pneumonia ◽  
Older Age ◽  
Single Center ◽  
Specific Antibodies ◽  
Factors Associated ◽  
Risk Of Cancer ◽  
Single Center Observational Study

Background:Myositis-specific antibodies (MSA) are highly specific and useful to classify patients as having syndromes with distinct clinical features and prognosis. MSA are almost always mutually exclusive and quite specific, adding value as a useful biomarker for diagnosis. Although individual autoantibodies aren’t sensitive enough to detect the full spectrum of idiopathic inflammatory myopathies (IIM), the sensitivity of a myositis panel is increasing as more autoantibodies are discovered, and as better assays become available.Objectives:We aimed to analyze the usefulness of a myositis-specific immunoblot for the diagnosis of IIM in a hospital cohort from January 2019 to December 2020. We also seek to correlate immunological findings with the risk of associated interstitial lung disease (ILD), cancer, or death.Methods:Retrospective single-center observational study conducted in a Spanish tertiary hospital. In patients with high clinical suspicion of IIM, a myositis immunoblot was performed including Jo1, PL-7, PL-12, EJ, SRP, Mi2, Ku, MDA-5, TIF1-γ, HMGCR, PM-Scl and Ro52 antibodies. The demographic characteristics, the risk of ILD, cancer and death were analyzed.Results:In a cohort of 313 patients with high suspicion of IIM, 87 patients (27.8%) presented a positive MSA (MSA+ve). The mean age at diagnosis was 56.7±16.9 years, with no significant differences between MSA+ve and MSA-ve (p=0.597). Most of the patients were women with significant differences between both groups (80.5% MSA+ve vs 68.1% MSA-ve, p=0.030).IIM were classified as antisynthetase syndrome (ARS) (38%), dermatomyositis (DM) (31%), overlap myopathy (OM) (16.9%) and necrotizing myopathy (NM) (14.1%) according to the manifestations and MSA found (Jo1, PL-12, PL-7, EJ in ARS; Mi-2, MDA-5 and TIF1-γ in DM; Ku and PM-Scl in OM; HMGCR and SRP in NM). The most frequent MSA were anti-Jo1 (16.9%), TIF1-γ (15.5%), Ku (12.7%), Mi-2 (9.9%), PL-7 (9.9%), HMCGR (8.5%), PL-12 (7%), MDA-5 (5.6%), SRP (5.6%) and EJ (4.2%). The presence of Ro52 associated with other MSA was found in 20 patients (22.9%).ILD was the most frequent manifestation (45.2% of the MSA+ve). Non-specific interstitial pneumonia (NSIP) was the most frequent ILD (39.5%), followed by usual interstitial pneumonia (34.2%). The main risk factors associated with IIM-ILD were some subtypes of the MSAs (p<0.001), the association of Ro52 (p<0.001), and older age (p=0.027). Among the IIM, ARS and OM (p<0.001) were more frequently associated with IIM-ILD. The MSAs most associated with IIM-ILD were Jo1, PL-7, PM-Scl, Ku and SRP (p<0.001).Cancer was found in 9.6% of MSA+ve patients. The most frequent tumors were gynecological (37.5%), followed by gastrointestinal (25%) and breast cancer (12.5%). Factors associated with cancer were age (p=0.010), TIF1-γ (p<0.001), SRP (p=0.004), PL-12 (p=0.013), PL-7 (p=0.047) and HMGCR (p=0.027).The mortality of these patients was 3.5%. There were no differences regarding MSA+ve/-ve (p = 0.911). However, MDA-5 (p=0.033) and older age (p=0.001) were associated with higher mortality. There were no significant differences between the IIM classifications, the associated SAD, the presence of cancer or ILD. However, longer follow-up periods and future studies are necessary to confirm these results.Conclusion:The use of a myositis blot allowed classifying, stratifying the risk of ILD, the risk of cancer and the risk of mortality in IIM. IIM-ILD was the most frequent complication, usually manifested as NSIP. The associated risk factors were ARS, OM, some MSAs, Ro52+ and older age. Cancer was a serious and frequent manifestation in these patients, especially in patients with TIF1-γ and other MSAs, so it is essential to know the risk factors and perform an early screening, especially in older patients.A better knowledge of the serological profiles of IIM will provide more individualized approaches and better risk stratification, helping in the management and treatment of these patients.References:[1]Satoh et al. Clin Rev Allergy Immunol. 2017 Feb;52(1):1-19.[2]Betteridge et al. J Intern Med. 2016 Jul;280(1):8-23.Disclosure of Interests:None declared

Incidence and risk factors for alopecia in survivors of critical illness: A multi-centre observational study

2019 ◽  
Vol 50 ◽  
pp. 31-35
Author(s):  
C.E. Battle ◽  
C. Lynch ◽  
C. Thorpe ◽  
S. Biggs ◽  
K. Grobbelaar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critical Illness ◽  
Observational Study

scholarly journals Predictive risk factors for worse outcomes in COVID-19 patients with different clinical features at baseline

2021 ◽  
Author(s):  
Elisabetta Schiaroli ◽  
Anna Gidari ◽  
Giovanni Brachelente ◽  
Sabrina Bastianelli ◽  
Alfredo Villa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Critical Illness ◽  
Serum Ferritin ◽  
Logistic Regression Analysis ◽  
Severe Disease ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Neutrophil Lymphocyte Ratio

IntroductionCOVID-19 is characterized by a wide range of clinical expression and by possible progression to critical illness and death. Therefore it is essential to identify risk factors predicting progression towards serious and fatal diseases. The aim of our study was to identify laboratory predictive markers of clinical progression in patients with moderate/severe disease and in those with acute respiratory distress syndrome (ARDS).Material and methodsUsing electronic medical records for all demographic, clinical and laboratory data, a retrospective study on all consecutive patients with COVID-19 admitted to the Infectious Disease Clinic of Perugia was performed. The PaO2/FiO2 ratio (P/F) assessment cut‑off of 200 mm Hg was used at baseline to categorize the patients into two clinical groups. The progression towards invasive ventilation and/or death was used to identify critical outcome. Statistical analysis was performed. Multivariate logistic regression analysis was adopted to identify risk factors of critical illness and mortality.ResultsIn multivariate logistic regression analysis neutrophil/lymphocyte ratio (NLR) was the only significant predictive factor of progression to a critical outcome (p = 0.03) and of in-hospital mortality (p = 0.03). In ARDS patients no factors were associated with critical progression. Serum ferritin > 1006 ng/ml was the only predictive value of critical outcome in COVID-19 subjects with moderate/severe disease (p = 0.02).ConclusionsNeutrophil/lymphocyte ratio and serum ferritin are the only biomarkers that can help to stratify the risk of severity and mortality in patients with COVID-19.

scholarly journals Development and Validation of a Nomogram to Predict Type 2 Diabetes Mellitus in Overweight and Obese Adults: A Prospective Cohort Study from 82938 Adults in China

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qingqing Liu ◽  
Jie Yuan ◽  
Maerjiaen Bakeyi ◽  
Jie Li ◽  
Zilong Zhang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Cohort Study ◽  
Prospective Cohort ◽  
Training Set ◽  
Obese Adults ◽  
Validation Set

Background. The twin epidemic of overweight/obesity and type 2 diabetes mellitus (T2DM) is a major public health problem globally, especially in China. Overweight/obese adults commonly coexist with T2DM, which is closely related to adverse health outcomes. Therefore, this study aimed to develop risk nomogram of T2DM in Chinese adults with overweight/obesity. Methods. We used prospective cohort study data for 82938 individuals aged ≥20 years free of T2DM collected between 2010 and 2016 and divided them into a training (n = 58056) and a validation set (n = 24882). Using the least absolute shrinkage and selection operator (LASSO) regression model in training set, we identified optimized risk factors of T2DM, followed by the establishment of T2DM prediction nomogram. The discriminative ability, calibration, and clinical usefulness of nomogram were assessed. The results were assessed by internal validation in validation set. Results. Six independent risk factors of T2DM were identified and entered into the nomogram including age, body mass index, fasting plasma glucose, total cholesterol, triglycerides, and family history. The nomogram incorporating these six risk factors showed good discrimination regarding the training set, with a Harrell’s concordance index (C-index) of 0.859 [95% confidence interval (CI): 0.850–0.868] and an area under the receiver operating characteristic curve of 0.862 (95% CI: 0.853–0.871). The calibration curves indicated well agreement between the probability as predicted by the nomogram and the actual probability. Decision curve analysis demonstrated that the prediction nomogram was clinically useful. The consistent of findings was confirmed using the validation set. Conclusions. The nomogram showed accurate prediction for T2DM among Chinese population with overweight and obese and might aid in assessment risk of T2DM.

scholarly journals Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Dose-Response ◽  
2019 ◽  
Vol 17 (4) ◽  
pp. 155932581989417 ◽  
Author(s):  
Zhi Huang ◽  
Jie Liu ◽  
Liang Luo ◽  
Pan Sheng ◽  
Biao Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Signaling Pathway ◽  
Risk Score ◽  
Low Risk ◽  
Training Data ◽  
The Cancer Genome Atlas ◽  
Training Set ◽  
Data Set ◽  
Validation Set ◽  
Cox Analysis

Background: Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC). Methods: Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis. Results: After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction. Conclusions: Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

A Biomarker Algorithm Defines Onset Grades of Acute Graft-Vs-Host Disease with Distinct Non-Relapse Mortality

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 661-661
Author(s):  
John E. Levine ◽  
Thomas M. Braun ◽  
Andrew C. Harris ◽  
Ernst Holler ◽  
Austin Taylor ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Risk Factors ◽  
Training Set ◽  
Host Disease ◽  
Plasma Biomarkers ◽  
Clinical Risk ◽  
Ann Arbor ◽  
Validation Set ◽  
Grade 3 ◽  
Multicenter Validation

Abstract The severity of symptoms at the onset of graft versus host disease (GVHD) does not accurately define risk, and thus most patients (pts) are treated alike with high dose systemic steroids. We hypothesized that concentrations of one or more plasma biomarkers at the time of GVHD diagnosis could define distinct non-relapse mortality (NRM) risk grades that could guide treatment in a multicenter setting. We first analyzed plasma that was prospectively collected at acute GVHD onset from 492 HCT pts from 2 centers, which we randomly divided into training (n=328) and validation (n=164) sets; 300 HCT pts who enrolled on multicenter BMT CTN primary GVHD therapy clinical trials provided a second validation set. We measured the concentrations of 3 prognostic biomarkers (TNFR1, REG3α, and ST2) and used competing risks regression to create an algorithm from the training set to compute a predicted probability (p) of 6 mo NRM from GVHD diagnosis where log[-log(1-p)] = -9.169 + 0.598(log2TNFR1) - 0.028(log2REG3α) + 0.189(log2ST2). We then rank ordered p from lowest to highest and identified thresholds that met predetermined criteria for 3 GVHD grades so that NRM would increase 15% on average with each grade. A range of thresholds in the training set met these criteria, and we chose one near each median to demarcate each grade. In the resulting grades, risk of NRM significantly increased with each grade after the onset of GVHD in both the training and validation sets (FIG 1A,B). Most (80%) NRM was due to steroid-refractory GI GVHD, even though surprisingly only half of these pts presented with GI symptoms. We next applied the biomarker algorithm and thresholds to the second multicenter validation set (n=300) and observed similarly significant differences in NRM (FIG 1C). Relapse, which was treated as a competing risk for NRM, did not differ among the three GVHD grades (Figure 1D-F). The differences in NRM thus translated into significantly different overall survival for each GVHD grade (Figure 1G-I). These differences in survival are explained by primary therapy response at day 28, which was highly statistically different for each of Ann Arbor grade (grade 1, 81%; grade 2, 68%; grade 3, 46%; p<0.001 for all comparisons). We performed additional analyses on the multicenter validation set of pts that developed GVHD after treatment with a wide spectrum of supportive care, conditioning and GVHD prophylaxis practices. As expected, the Glucksberg grade at GVHD onset did not correlate with NRM (data not shown). Despite small sample sizes, the same biomarker algorithm and thresholds defined three distinct risk strata for NRM within each Glucksberg grade (FIG 2A-C). Pts with the higher Ann Arbor grades were usually less likely to respond to treatment. Unexpectedly, approximately the same proportion of pts were assigned to each Ann Arbor grade (~25% grade 1, ~55% grade 2, ~20% grade 3) regardless of the Glucksberg grade (FIG 2D-F). Several clinical risk factors, such as donor type, age, conditioning, and HLA-match, can predict treatment response and survival in patients with GVHD. Using Ann Arbor grade 2 as a reference, we found that Ann Arbor grade 1 predicted a lower risk of NRM (range 0.16-0.32) and grade 3 a higher risk of NRM (range 1.4-2.9), whether or not any of these clinical risk factors were present. To directly compare Ann Arbor grades to Glucksberg grades, we fit a multivariate model with simultaneous adjustment for both grades. FIG 3 shows that Ann Arbor grade 3 pts had significantly higher risk for NRM (p=0.005) and Ann Arbor grade 1 pts had significantly less risk for NRM (p=0.002) than pts with Ann Arbor grade 2. By contrast, the confidence intervals for the HRs of the Glucksberg grades encompassed 1.0, demonstrating a lack of statistical significance between grades. In conclusion, we have developed and validated an algorithm of plasma biomarkers that define three grades of GVHD with distinct risks of NRM and treatment failure despite differences in clinical severity at presentation. The biomarkers at GVHD onset appear to reflect GI tract disease activity that does not correlate with GI symptom severity at the time. This algorithm may be useful in clinical trial design. For example, it can exclude pts who are likely to respond to standard therapy despite severe clinical presentations, thus limiting the exposure of low risk pts to investigational agents while also identifying the high risk pts most likely to benefit from investigational approaches. Figure 1 Figure 1. Figure 2 Figure 2. Figure 3 Figure 3. Disclosures Levine: University of Michigan: GVHD biomarker patent Patents & Royalties. Braun:University of Michigan: GVHD biomarker patent Patents & Royalties. Ferrara:University of Michigan: GVHD biomarker patent Patents & Royalties.

Endoscopic third ventriculostomy in the treatment of childhood hydrocephalus in Uganda: report of a scoring system that predicts success

2010 ◽  
Vol 5 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Benjamin C. Warf ◽  
John Mugamba ◽  
Abhaya V. Kulkarni
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Endoscopic Third Ventriculostomy ◽  
Successful Outcome ◽  
Third Ventriculostomy ◽  
P Value ◽  
Training Set ◽  
Childhood Hydrocephalus ◽  
Validation Set

Object In Uganda, childhood hydrocephalus is common and difficult to treat. In some children, endoscopic third ventriculostomy (ETV) can be successful and avoid dependence on a shunt. This can be especially beneficial in Uganda, because of the high risk of infection and long-term failure associated with shunting. Therefore, the authors developed and validated a model to predict the chances of ETV success, taking into account the unique characteristics of a large sub-Saharan African population. Methods All children presenting with hydrocephalus at CURE Children's Hospital of Uganda (CCHU) between 2001 and 2007 were offered ETV as first-line treatment and were prospectively followed up. A multivariable logistic regression model was built using ETV success at 6 months as the outcome. The model was derived on 70% of the sample (training set) and validated on the remaining 30% (validation set). Results Endoscopic third ventriculostomy was attempted in 1406 patients. Of these, 427 were lost to follow-up prior to 6 months. In the remaining 979 patients, the ETV was aborted in 281 due to poor anatomy/visibility and in 310 the ETV failed during the first 6 months. Therefore, a total of 388 of 979 (39.6% and [55.6% of completed ETVs]) procedures were successful at 6 months. The mean age at ETV was 12.6 months, and 57.8% of cases were postinfectious in origin. The authors' logistic regression model contained the following significant variables: patient age at ETV, cause of hydrocephalus, and whether choroid plexus cauterization was performed. In the training set (676 patients) and validation set (303 patients), the model was able to accurately predict the probability of successful ETV (Hosmer-Lemeshow p value > 0.60 and C statistic > 0.70). The authors developed the simplified CCHU ETV Success Score that can be used in the field to predict the probability of ETV success. Conclusions The authors' model will allow clinicians to accurately identify children with a good chance of successful outcome with ETV, taking into account the unique characteristics and circumstances of the Ugandan population.

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