A comparison of CA125, HE4, risk ovarian malignancy algorithm (ROMA), and risk malignancy index (RMI) for the classification of ovarian masses

OBJECTIVE: Differentiation between benign and malignant ovarian neoplasms is essential for creating a system for patient referrals. Therefore, the contributions of the tumor markers CA125 and human epididymis protein 4 (HE4) as well as the risk ovarian malignancy algorithm (ROMA) and risk malignancy index (RMI) values were considered individually and in combination to evaluate their utility for establishing this type of patient referral system. METHODS: Patients who had been diagnosed with ovarian masses through imaging analyses (n = 128) were assessed for their expression of the tumor markers CA125 and HE4. The ROMA and RMI values were also determined. The sensitivity and specificity of each parameter were calculated using receiver operating characteristic curves according to the area under the curve (AUC) for each method. RESULTS: The sensitivities associated with the ability of CA125, HE4, ROMA, or RMI to distinguish between malignant versus benign ovarian masses were 70.4%, 79.6%, 74.1%, and 63%, respectively. Among carcinomas, the sensitivities of CA125, HE4, ROMA (pre-and post-menopausal), and RMI were 93.5%, 87.1%, 80%, 95.2%, and 87.1%, respectively. The most accurate numerical values were obtained with RMI, although the four parameters were shown to be statistically equivalent. CONCLUSION: There were no differences in accuracy between CA125, HE4, ROMA, and RMI for differentiating between types of ovarian masses. RMI had the lowest sensitivity but was the most numerically accurate method. HE4 demonstrated the best overall sensitivity for the evaluation of malignant ovarian tumors and the differential diagnosis of endometriosis. All of the parameters demonstrated increased sensitivity when tumors with low malignancy potential were considered low-risk, which may be used as an acceptable assessment method for referring patients to reference centers.

Leprosy reactions: coinfections as a possible risk factor

OBJECTIVE: This study aimed to determine the frequency of coinfections in leprosy patients and whether there is a relationship between the presence of coinfections and the development of leprosy reactional episodes. METHOD: A cross-sectional study based on an analysis of the medical records of the patients who were treated at the Leprosy Clinics of the Ribeirao Preto Medical School, University of Sao Paulo, was conducted from 2000 to 2010. Information was recorded regarding the age, sex, clinical status, WHO classification, treatment, presence of reactions and coinfections. Focal and systemic infections were diagnosed based on the history, physical examination, and laboratory tests. Multinomial logistic regression was used to evaluate the associations between the leprosy reactions and the patients' gender, age, WHO classification and coinfections. RESULTS: Two hundred twenty-five patients were studied. Most of these patients were males (155/225 = 68.8%) of an average age of 49.31 +/- 15.92 years, and the most prevalent clinical manifestation was the multibacillary (MB) form (n = 146), followed by the paucibacillary (PB) form (n = 79). Erythema nodosum leprosum (ENL) was more prevalent (78/122 = 63.9%) than the reversal reaction (RR) (44/122 = 36.1%), especially in the MB patients (OR 5.07; CI 2.86-8.99; p<0.0001) who exhibited coinfections (OR 2.26; CI 1.56-3.27; p<0.0001). Eighty-eight (88/225 = 39.1%) patients exhibited coinfections. Oral coinfections were the most prevalent (40/88 = 45.5%), followed by urinary tract infections (17/88 = 19.3%), sinusopathy (6/88 = 6.8%), hepatitis C (6/88 = 6.8%), and hepatitis B (6/88 = 6.8%). CONCLUSIONS: Coinfections may be involved in the development and maintenance of leprosy reactions.

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

Background: Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods: Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results: We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions: The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.

Covariates of high-risk human papillomavirus (HPV) infections are distinct for incident CIN1, CIN2 and CIN3 as disclosed by competing-risks regression models

Background: In addition to the oncogenic human papillomavirus (HPV), several cofactors are needed in cervical carcinogenesis, but whether the HPV covariates associated with incident i) CIN1 are different from those of incident ii) CIN2 and iii) CIN3 needs further assessment. Objectives: To gain further insights into the true biological differences between CIN1, CIN2 and CIN3, we assessed HPV covariates associated with incident CIN1, CIN2, and CIN3. Study Design and Methods: HPV covariates associated with progression to CIN1, CIN2 and CIN3 were analysed in the combined cohort of the NIS (n = 3,187) and LAMS study (n = 12,114), using competing-risks regression models (in panel data) for baseline HR-HPV-positive women (n = 1,105), who represent a sub-cohort of all 1,865 women prospectively followed-up in these two studies. Results: Altogether, 90 (4.8%), 39 (2.1%) and 14 (1.4%) cases progressed to CIN1, CIN2, and CIN3, respectively. Among these baseline HR-HPV-positive women, the risk profiles of incident GIN I, CIN2 and CIN3 were unique in that completely different HPV covariates were associated with progression to CIN1, CIN2 and CIN3, irrespective which categories (non-progression, CIN1, CIN2, CIN3 or all) were used as competing-risks events in univariate and multivariate models. Conclusions: These data confirm our previous analysis based on multinomial regression models implicating that distinct covariates of HR-HPV are associated with progression to CIN1, CIN2 and CIN3. This emphasises true biological differences between the three grades of GIN, which revisits the concept of combining CIN2 with CIN3 or with CIN1 in histological classification or used as a common end-point, e.g., in HPV vaccine trials.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Child Physical Abuse: Evaluating Psychological Risk Factors in Accused Caregivers

It was verified to what extent cognitive and affective/emotional variables could distinguish caregivers accused of committing physical abuse (G1) from those without physical abuse records (G2). The Child Abuse Potential Inventory (CAP), which is an instrument designed to assess psychological risk factors in caregivers, was used. A questionnaire on socio-demographic characterization and another on economic classification were also employed to equate the groups. G1 presented a greater potential risk than G2, higher levels of Distress, Rigidity, Problems with the Child and with Themselves, Problems with Others, and a lower level of Ego Strength. These variables contribute with the composition of physical abuse risk, since, in agreement with the Social Information Processing Model, they would be related to cognitive and affective basic processes which are veiled to the perceptions and evaluation/interpretations, associated to abusive parental behavior.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Risk factors associated with death in Brazilian children with severe dengue: a case-control study

Objective: The purpose of this case-control study was to evaluate risk factors associated with death in children with severe dengue. Methods: The clinical condition of hospitalized patients with severe dengue who died (cases, n = 18) was compared with that of hospitalized patients with severe dengue who survived (controls, n = 77). The inclusion criteria for this study were age under 13 years; hospital admission in São Luis, northeastern Brazil; and laboratory-confirmed diagnosis of dengue. Results: Severe bleeding (hemoptysis), a defining criterion for dengue severity, was the factor most strongly associated with death in our study. We also found that epistaxis and persistent vomiting, both included as warning signs in the World Health Organization (WHO) classification of dengue, were strongly associated with death. No significant association was observed between any of the laboratory findings and death. Conclusions: The finding that epistaxis and persistent vomiting were also associated with death in children with severe dengue was unexpected and deserves to be explored in future studies. Because intensive care units are often limited in resource-poor settings, any information that can help to distinguish patients with severe dengue with a higher risk to progress to death may be crucial.