Statistical analysis of pathologic risk factors for intramyometrial lymphvascular space involvement in myoinvasive endometrial carcinoma

2006 ◽  
Vol 16 (3) ◽  
pp. 1330-1335 ◽  
Author(s):  
L. H. Honorè ◽  
J. Hanson

In a retrospective study using univariate analysis, we identified tumor type (nonendometrioid vs endometrioid), depth of myoinvasion (MI), mode of MI (infiltrative vs cohesive), and direct anatomic invasion of the cervical wall from the isthmus as significant positive risk factors for intramyometrial lymphvascular space involvement (LVSI). On multivariate analysis, tumor grade, depth of MI, and mode of MI retained their significance. We created a grid for the relative risks of LVSI with respect to these variables individually or in combination. We suggest that our indirect estimate of the risk of LVSI can help in assessing prognosis and determining the need for adjuvant therapy whenever LVSI is important in clinical decision making, but its pathologic diagnosis is uncertain.

Author(s):  
Tiffany Shaw ◽  
Eric Prommer

Delirium is a frequent event in patients with advanced cancer. Untreated delirium affects assessment of symptoms, impairs communication including participation in clinical decision-making. This study used specific diagnostic criteria for delirium and prospectively identified precipitating causes of delirium. The study identified factors associated with reversible and irreversible delirium. Impact of delirium on prognosis was evaluated. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case. Topics covered include delirium, neoplasms, palliative care, polypharmacy, risk factors, and therapeutics.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaoling Zhong ◽  
◽  
Rongqin Yu ◽  
Robert Cornish ◽  
Xiaoping Wang ◽  
...  

Abstract Background Violence risk assessment is a routine part of clinical services in mental health, and in particular secure psychiatric hospitals. The use of prediction models and risk tools can assist clinical decision-making on risk management, including decisions about further assessments, referral, hospitalization and treatment. In recent years, scalable evidence-based tools, such as Forensic Psychiatry and Violent Oxford (FoVOx), have been developed and validated for patients with mental illness. However, their acceptability and utility in clinical settings is not known. Therefore, we conducted a clinical impact study in multiple institutions that provided specialist mental health service. Methods We followed a two-step mixed-methods design. In phase one, we examined baseline risk factors on 330 psychiatric patients from seven forensic psychiatric institutes in China. In phase two, we conducted semi-structured interviews with 11 clinicians regarding violence risk assessment from ten mental health centres. We compared the FoVOx score on each admission (n = 110) to unstructured clinical risk assessment and used a thematic analysis to assess clinician views on the accuracy and utility of this tool. Results The median estimated probability of violent reoffending (FoVOx score) within 1 year was 7% (range 1–40%). There was fair agreement (72/99, 73% agreement) on the risk categories between FoVOx and clinicians’ assessment on risk categories, and moderate agreement (10/12, 83% agreement) when examining low and high risk categories. In a majority of cases (56/101, 55%), clinicians thought the FoVOx score was an accurate representation of the violent risk of an individual patient. Clinicians suggested some additional clinical, social and criminal risk factors should be considered during any comprehensive assessment. In addition, FoVOx was considered to be helpful in assisting clinical decision-making and individual risk assessment. Ten out of 11 clinicians reported that FoVOx was easy to use, eight out of 11 was practical, and all clinicians would consider using it in the future. Conclusions Clinicians found that violence risk assessment could be improved by using a simple, scalable tool, and that FoVOx was feasible and practical to use.


Author(s):  
Timothy S Chang ◽  
Yi Ding ◽  
Malika K Freund ◽  
Ruth Johnson ◽  
Tommer Schwarz ◽  
...  

SummaryWith the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-control study, we used de-identified electronic health records (EHR) from the University of California Los Angeles (UCLA) Health System between March 9th, 2020 and June 14th, 2020 to identify risk factors for COVID-19 susceptibility (severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) PCR test positive), inpatient admission, and severe outcomes (treatment in an intensive care unit or intubation). Of the 26,602 individuals tested by PCR for SARS-CoV-2, 992 were COVID-19 positive (3.7% of Tested), 220 were admitted in the hospital (22% of COVID-19 positive), and 77 had a severe outcome (35% of Inpatient). Consistent with previous studies, males and individuals older than 65 years old had increased risk of inpatient admission. Notably, individuals self-identifying as Hispanic or Latino constituted an increasing percentage of COVID-19 patients as disease severity escalated, comprising 24% of those testing positive, but 40% of those with a severe outcome, a disparity that remained after correcting for medical co-morbidities. Cardiovascular disease, hypertension, and renal disease were premorbid risk factors present before SARS-CoV-2 PCR testing associated with COVID-19 susceptibility. Less well-established risk factors for COVID-19 susceptibility included pre-existing dementia (odds ratio (OR) 5.2 [3.2-8.3], p=2.6 × 10−10), mental health conditions (depression OR 2.1 [1.6-2.8], p=1.1 × 10−6) and vitamin D deficiency (OR 1.8 [1.4-2.2], p=5.7 × 10−6). Renal diseases including end-stage renal disease and anemia due to chronic renal disease were the predominant premorbid risk factors for COVID-19 inpatient admission. Other less established risk factors for COVID-19 inpatient admission included previous renal transplant (OR 9.7 [2.8-39], p=3.2×10−4) and disorders of the immune system (OR 6.0 [2.3, 16], p=2.7×10−4). Prior use of oral steroid medications was associated with decreased COVID-19 positive testing risk (OR 0.61 [0.45, 0.81], p=4.3×10−4), but increased inpatient admission risk (OR 4.5 [2.3, 8.9], p=1.8×10−5). We did not observe that prior use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers increased the risk of testing positive for SARS-CoV-2, being admitted to the hospital, or having a severe outcome. This study involving direct EHR extraction identified known and less well-established demographics, and prior diagnoses and medications as risk factors for COVID-19 susceptibility and inpatient admission. Knowledge of these risk factors including marked ethnic disparities observed in disease severity should guide government policies, identify at-risk populations, inform clinical decision making, and prioritize future COVID-19 research.


2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Qinrui Hu ◽  
Yujing Bai ◽  
Xiaoli Chen ◽  
Lvzhen Huang ◽  
Yi Chen ◽  
...  

Objective. To determine the prevalence and risk factors for the recurrence of retinopathy of prematurity (ROP) in Zone II Stage 3+ after ranibizumab treatment.Methods. This was a retrospective, nonrandomized, noncontrolled study that excluded Zone I and aggressive posterior ROP (APROP) cases. Infants who developed Zone II Stage 3 ROP with plus disease and underwent initial intravitreal injection of ranibizumab (IVR) were recruited. Patients were divided into 2 groups based on the outcome after initial ranibizumab treatment: recurrence of ROP or favorable outcome. Data was collected and analyzed by SPSS 16.0.Results. Forty-two patients were included, and 80 eyes with Zone II Stage 3+ were subjected to IVR treatment. Eleven of 42 patients (26.2%, 18 eyes) had a recurrence of ROP after the initial treatment. On univariate analysis, preretinal hemorrhage before treatment was significantly different between the two groups (P=0.000). Multivariate analysis found that preretinal hemorrhage before treatment was the only factor associated with the recurrence of ROP in our study (P=0.004).Conclusions. The recurrence rate of ROP in Zone II Stage 3+ after initial ranibizumab treatment was notable and preretinal hemorrhage before treatment was associated with the recurrence of ROP in our study.


Assessment ◽  
2020 ◽  
pp. 107319112093916
Author(s):  
Ewa K. Czyz ◽  
Jamie R.T. Yap ◽  
Cheryl A. King ◽  
Inbal Nahum-Shani

Mobile technology offers new possibilities for assessing suicidal ideation and behavior in real- or near-real-time. It remains unclear how intensive longitudinal data can be used to identify proximal risk and inform clinical decision making. In this study of adolescent psychiatric inpatients ( N = 32, aged 13-17 years, 75% female), we illustrate the application of a three-step process to identify early signs of suicide-related crises using daily diaries. Using receiver operating characteristic (ROC) curve analyses, we considered the utility of 12 features—constructed using means and variances of daily ratings for six risk factors over the first 2 weeks postdischarge (observations = 360)—in identifying a suicidal crisis 2 weeks later. Models derived from single risk factors had modest predictive accuracy (area under the ROC curve [AUC] 0.46-0.80) while nearly all models derived from combinations of risk factors produced higher accuracy (AUCs 0.80-0.91). Based on this illustration, we discuss implications for clinical decision making and future research.


2021 ◽  
Vol 12 (20) ◽  
pp. 6050-6057
Author(s):  
Xiaoyuan Liang ◽  
Wei Cai ◽  
Xingyu Liu ◽  
Ming Jin ◽  
Lingxiang Ruan ◽  
...  

2021 ◽  
Author(s):  
Abhinav Vepa ◽  
Amer Saleem ◽  
Kambiz Rakhshan ◽  
Amr Omar ◽  
Diana Dharmaraj ◽  
...  

AbstractIntroductionWithin the UK, COVID-19 has contributed towards over 103,000 deaths. Multiple risk factors for COVID-19 have been identified including various demographics, co-morbidities, biochemical parameters, and physical assessment findings. However, using this vast data to improve clinical care has proven challenging.Aimsto develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, to aid risk-stratification and earlier clinical decision-making.MethodsAnonymized data regarding 44 independent predictor variables of 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-controlled analysis. Primary outcomes included inpatient mortality, level of ventilatory support and oxygen therapy required, and duration of inpatient treatment. Secondary pulmonary embolism was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were created using Bayesian Networks, and cross-validated.ResultsOur multivariable models were able to predict, using feature selected risk factors, the probability of inpatient mortality (F1 score 83.7%, PPV 82%, NPV 67.9%); level of ventilatory support required (F1 score varies from 55.8% “High-flow Oxygen level” to 71.5% “ITU-Admission level”); duration of inpatient treatment (varies from 46.7% for “≥ 2 days but < 3 days” to 69.8% “≤ 1 day”); and risk of pulmonary embolism sequelae (F1 score 85.8%, PPV of 83.7%, and NPV of 80.9%).ConclusionOverall, our findings demonstrate reliable, multivariable predictive models for 4 outcomes, that utilize readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as clinical decision-making tools.HighlightsUsing COVID-19 risk-factor data to assist clinical decision making is a challengeAnonymous data from 355 COVID-19 inpatients was collected & balancedKey independent variables were feature selected for 4 different outcomesAccurate, multi-variable predictive models were computed, using Bayesian NetworksFuture research should externally validate our models & demonstrate clinical utility


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