Administrative Data Fail to Accurately Identify Cases of Healthcare-Associated Infection

2006 ◽  
Vol 27 (4) ◽  
pp. 332-337 ◽  
Author(s):  
Eileen R. Sherman ◽  
Kateri H. Heydon ◽  
Keith H. St. John ◽  
Eva Teszner ◽  
Susan L. Rettig ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Administrative Data ◽  
Active Surveillance ◽  
Predictive Value ◽  
Healthcare Associated Infection ◽  
Cross Sectional ◽  
Policy Makers ◽  
Predictive Values ◽  
Healthcare Associated ◽  
Very High

Objective.Some policy makers have embraced public reporting of healthcare-associated infections (HAIs) as a strategy for improving patient safety and reducing healthcare costs. We compared the accuracy of 2 methods of identifying cases of HAI: review of administrative data and targeted active surveillance.Design, Setting, and Participants.A cross-sectional prospective study was performed during a 9-month period in 2004 at the Children's Hospital of Philadelphia, a 418-bed academic pediatric hospital. “True HAI” cases were defined as those that met the definitions of the National Nosocomial Infections Surveillance System and that were detected by a trained infection control professional on review of the medical record. We examined the sensitivity and the positive and negative predictive values of identifying HAI cases by review of administrative data and by targeted active surveillance.Results.We found similar sensitivities for identification of HAI cases by review of administrative data (61%) and by targeted active surveillance (76%). However, the positive predictive value of identifying HAI cases by review of administrative data was poor (20%), whereas that of targeted active surveillance was 100%.Conclusions.The positive predictive value of identifying HAI cases by targeted active surveillance is very high. Additional investigation is needed to define the optimal detection method for institutions that provide HAI data for comparative analysis.

scholarly journals Evaluating Specivity, Sensitivity, Positive and Negative Predictive Values of CA125 for Diagnosing Ovarian Cancer

2021 ◽  
Vol 24 (2) ◽  
pp. 196-203
Author(s):  
Elahe Fini ◽  
◽  
Neda Nasirian ◽  
Bahram Hosein Beigy ◽  
◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Positive Predictive Value ◽  
Tumor Marker ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Screening Test ◽  
High Sensitivity ◽  
Medical Community ◽  
Cross Sectional ◽  
Predictive Values

Background and Aim: Ovarian cancer is among the most common cancers in women worldwide. CA125 is the most frequent biomarker used in the screening for ovarian cancer. CA125 has no high sensitivity and specificity as a screening test in the medical community; however, because of being simple and noninvasive, it is almost always requested for evaluation and ruling out cancer. It plays an important role in the treatment and post-treatment process, the prediction of prognosis, and the relapse of the disease. The present study aimed to determine the relationship between a high level of CA125 tumor marker and ovarian cancer by detecting spesivity, sensivity, positive and negative predictive values. Methods & Materials: In this cross-sectional study, all cases undergoing CA125 test in Velayat Hospital in 2017-1028 were evaluated for having ovarian cancer. In addition, the CA125 level was compared between healthy individuals and patients with ovarian cancer. Finally, the obtained data were analyzed using SPSS. Ethical Considerations: The present study was approved by the Qazvin University of Medical Sciences (Ethics Code: IR.QUMS.REC.1396.316). Results: In this study, 35.3% of the study participants received a definite diagnosis of ovarian cancer. Generally, CA125 values were negative in 41.8% and positive in.58.2% of the study subjects. The sensitivity of the test was measured as 80.1%, the specivity as 53.6%, the positive predictive value equaled 48.4%, and the negative predictive value was measured as 83%. There was a significant relationship between age and the presence of ovarian cancer, and serum CA125 levels. Conclusion: The present study suggested that age and the serum level of CA125 were statistically significant. Finally, CA125 levels were significantly related to ovarian cancer. It provided moderate specivity and specivity as well as low positive predictive value and high negative predictive value as a tumor marker; it is valuable for ruling out of tumor but not appropriate as a screening test.

scholarly journals Diagnostic Value of CK19, HBME-1 and TROP2 Biomarkers in Identification of Different Types of Thyroid Follicular Neoplasms

2021 ◽  
Author(s):  
Shohreh Vojuhi ◽  
Masoud Mohebbi ◽  
Zohreh Mousavi ◽  
Mohammadali Yaghoubi ◽  
Reza Ziaolhagh
Keyword(s):  
Positive Predictive Value ◽  
Papillary Carcinoma ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Follicular Adenoma ◽  
Thyroid Neoplasms ◽  
Diagnostic Value ◽  
Cross Sectional ◽  
Predictive Values ◽  
Different Types

Thyroid malignancies are found in 7% to 15% of all thyroid nodules. Immunohistochemical markers, including CK19, HBME-1and TROP2, have shown an effective role in identifying these malignancies. Hence, due to the lack of appropriate diagnostic tests for the identification of thyroid neoplasms, in this study, we aimed to determine the diagnostic value of these biomarkers in the identification of different types of follicular thyroid neoplasms. In this cross-sectional study, paraffin-embedded tissue blocks from the surgical resection of patients with thyroid nodules, referring to Imam Reza and Razavi Hospitals of Mashhad in 2017, were studied. Sensitivity, specificity, and positive and negative predictive values of these biomarkers for the identification of different types of follicular thyroid neoplasms were also studied. 129 patients with a mean age of 44.65±12.59 years participated in this study, of whom 101 (78.29%) were women. The most common type of follicular thyroid neoplasm was papillary carcinoma (60.47%). The highest sensitivity (94.87%) and positive predictive value (68.51%) in the detection of follicular neoplasms was observed by CK19 in papillary carcinoma. The sensitivity and positive predictive value of TROP2 in the detection of papillary neoplasms was 93.58% and 75.25%, respectively. In addition, HBME-1 had the highest specificity (72.54 %) and positive predictive value (81.57%) in identifying this neoplasm. The results of this study showed that CK19, HBME-1, and TROP2 had high diagnostic value in the detection of papillary thyroid neoplasms. Although these biomarkers had low diagnostic value in identifying follicular adenoma and carcinoma, given the high negative predictive value, they can be considered as powerful markers in identifying negative cases.

scholarly journals Volume of sputum to detect acid-fast bacilli as a measure of quality for the diagnosis of pulmonary tuberculosis at the Dr George Mukhari Hospital, South Africa

2011 ◽  
Vol 3 (1) ◽  
Author(s):  
Iqbal Rashid ◽  
Langalibalele H. Mabuza ◽  
Indiran Govender ◽  
Deidre Pretorius
Keyword(s):  
South Africa ◽  
Pulmonary Tuberculosis ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
P Value ◽  
Likelihood Ratios ◽  
Cross Sectional ◽  
Predictive Values ◽  
Acid Fast Bacilli

Background: Optimum sputum results for acid-fast bacilli (AFB) microscopy are linked to a sputum quantity of at least 5.0 mL. This study was aimed at establishing the effect of sputum quantity in the pick-up rate of AFB microscopy by comparing sputum samples of 5.0 mL and 2.0 mL.Methods: An analytical cross-sectional study was carried out at the Dr George Mukhari Hospital (DGMH) in Pretoria, South Africa, from 05 January 2007 to 04 January 2008.Two sputum samples, 5.0 mL and 2.0 mL, were collected from each of the 330 adult PTB (pulmonary tuberculosis) suspects. Fluorescence microscopy was used in the sputum analysis. The yield through microscopy of the 2.0 mL specimen versus the 5.0 mL specimen was compared and analysed, using culture results as the gold standard.Results: From a sample of 330 specimens, 77 tested AFB positive on microscopy. In the 5.0 mL samples, the sensitivity was 76.6% (95% CI, 66.0% – 84.7%), specificity 99.6% (95% CI 97.8% – 99.9%), positive predictive value (PV+) 98.3% (95% CI 91.1% – 99.7%), negative predictive value (PV-) 93.3% (95% CI 89.7% – 95.7%), the likelihood ratio (LR) for a positive microscopy 192 and the LR for a negative test was 0.23. In the 2.0 mL specimens, the sensitivity was 75.3% (95% CI 64.6% – 83.6%), specificity 99.2% (95% CI 97.1% – 99.8%), positive predictive value (PV+) 96.7% (95% CI 88.6% – 99.1%), negative predictive value (PV-) 93.0% (95% CI 89.3% – 95.4%), the LR for a positive microscopy was 94 and 0.25 for a negative microscopy. There was a statistically significant association (p-value < 0.001) between the microscopy and culture tests in both the 5.0 mL and the 2.0 mL specimen categories. The strength of association between the microscopy and culture, as indicated by the kappa test was 0.83 and 0.81 in the 5.0 mL and 2.0 mL categories, respectively.Conclusion: Compared to the 2.0 mL specimen category, the yield for AFB microscopy in the 5.0 mL specimen category was consistently superior, as indicated by the higher sensitivity, specificity, predictive values and the likelihood ratios in the 5.0 mL specimen category. It is recommended that sputum specimen collection for AFB microscopy should aim for a minimum volume of 5.0 mL.

Validity of administrative database code algorithms to identify vascular access placement, surgical revisions, and secondary patency

2018 ◽  
Vol 19 (6) ◽  
pp. 561-568 ◽  
Author(s):  
Ahmed A Al-Jaishi ◽  
Louise M Moist ◽  
Matthew J Oliver ◽  
Danielle M Nash ◽  
Jamie L Fleet ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Administrative Data ◽  
Vascular Access ◽  
Predictive Value ◽  
Catheter Insertion ◽  
Administrative Databases ◽  
Secondary Patency ◽  
Operating Characteristics ◽  
Predictive Values ◽  
Arteriovenous Access

Background: We assessed the validity of physician billing codes and hospital admission using International Classification of Diseases 10th revision codes to identify vascular access placement, secondary patency, and surgical revisions in administrative data. Methods: We included adults (≥18 years) with a vascular access placed between 1 April 2004 and 31 March 2013 at the University Health Network, Toronto. Our reference standard was a prospective vascular access database (VASPRO) that contains information on vascular access type and dates of placement, dates for failure, and any revisions. We used VASPRO to assess the validity of different administrative coding algorithms by calculating the sensitivity, specificity, and positive predictive values of vascular access events. Results: The sensitivity (95% confidence interval) of the best performing algorithm to identify arteriovenous access placement was 86% (83%, 89%) and specificity was 92% (89%, 93%). The corresponding numbers to identify catheter insertion were 84% (82%, 86%) and 84% (80%, 87%), respectively. The sensitivity of the best performing coding algorithm to identify arteriovenous access surgical revisions was 81% (67%, 90%) and specificity was 89% (87%, 90%). The algorithm capturing arteriovenous access placement and catheter insertion had a positive predictive value greater than 90% and arteriovenous access surgical revisions had a positive predictive value of 20%. The duration of arteriovenous access secondary patency was on average 578 (553, 603) days in VASPRO and 555 (530, 580) days in administrative databases. Conclusion: Administrative data algorithms have fair to good operating characteristics to identify vascular access placement and arteriovenous access secondary patency. Low positive predictive values for surgical revisions algorithm suggest that administrative data should only be used to rule out the occurrence of an event.

scholarly journals Identification of the Most Appropriate Site for the Cold Test in Molar Teeth

2017 ◽  
Vol 20 (1) ◽  
pp. 79-88
Author(s):  
Brenda E. Castillo-Silva MSc, PhD ◽  
Nuria Patiño-Marín MSc, PhD ◽  
Gabriel Alejandro Martínez-Castañón MSc, PhD ◽  
Carlo E. Medina-Solís DDS, MS ◽  
Norma Verónica Zavala Alonso MSc, PhD ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Cold Test ◽  
Cross Sectional ◽  
Posterior Teeth ◽  
Predictive Values ◽  
Second Molar ◽  
Molar Teeth ◽  
Mandibular Second Molar

A test frequently used to complement endodontic diagnoses is the cold test; however, in the last 20 years, authors have reported incorrect results within pulp sensitivity tests. Specifically, a high frequency of false results in posterior teeth, were found. The aim of this study was to identify the most appropriate site for the cold test in molar teeth with a need for endodontic treatment, calculating predictive values, accuracy and reproducibility. A cross-sectional study was performed, evaluating 390 subjects. A total of 152 subjects of both genders from the ages of 15-65 years old participated. The ideal standard was established by direct pulp inspection, and the cold test agent used was 1,1,1,2-tetrafluoroethane. The patients were divided into four groups in relation to the molar tooth: (1) mandibular first molar, (2) mandibular second molar, (3) maxillary first molar, and (4) maxillary second molar. 169 teeth and 676 sites were studied. (a) The most appropriate sites for cold test were the middle third of the buccal surface and cervical third of the buccal surface in the mandibular molars with the following results: Middle third of the first molar: Accuracy 0.93, positive predictive value 0.90 and negative predictive value 0.96. Middle third of the second molar: Accuracy 0.93, positive predictive value 1.00 and negative predictive value 0.90. In relation to third cervical the results were: First molar: Accuracy 0.93, positive predictive value 0.89 and negative predictive value 0.97 y second molar: Accuracy 0.93, positive predictive value 1.00 and negative predictive value 0.90. (b) The highest reproducibility was observed in the middle third of the buccal surface with cervical third of the buccal surface in the mandibular second molar (1.00). The most appropriate site and reproducibility of the sites are auxiliary to complement endodontic diagnose with the cold test.

scholarly journals Positive predictive values of Breast Imaging Reporting and Data System (BI-RADS®) categories 3, 4 and 5 in breast lesions submitted to percutaneous biopsy

2013 ◽  
Vol 46 (4) ◽  
pp. 209-213 ◽  
Author(s):  
Gustavo Machado Badan ◽  
Décio Roveda Júnior ◽  
Carlos Alberto Pecci Ferreira ◽  
Felipe Augusto Trocoli Ferreira ◽  
Eduardo de Faria Castro Fleury ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Breast Imaging ◽  
Percutaneous Biopsy ◽  
Cross Sectional Study ◽  
Breast Lesions ◽  
Cross Sectional ◽  
Predictive Values ◽  
Histological Results ◽  
The City

Objective To evaluate the BI-RADS as a predictive factor of suspicion for malignancy in breast lesions by correlating radiological with histological results and calculating the positive predictive value for categories 3, 4 and 5 in a breast cancer reference center in the city of São Paulo. Materials and Methods Retrospective, analytical and cross-sectional study including 725 patients with mammographic and/or sonographic findings classified as BI-RADS categories 3, 4 and 5 who were referred to the authors' institution to undergo percutaneous biopsy. The tests results were reviewed and the positive predictive value was calculated by means of a specific mathematical equation. Results Positive predictive values found for categories 3, 4 and 5 were respectively the following: 0.74%, 33.08% and 92.95%, for cases submitted to ultrasound-guided biopsy, and 0.00%, 14.90% and 100% for cases submitted to stereotactic biopsy. Conclusion The present study demonstrated high suspicion for malignancy in lesions classified as category 5 and low risk for category 3. As regards category 4, the need for systematic biopsies was observed.

scholarly journals Serum procalcitonin as a biomarker of sepsis

2021 ◽  
Vol 8 (10) ◽  
pp. 1494
Author(s):  
Nyamnyei Konyak ◽  
Linda Marangmei ◽  
Medo M. Kuotsu ◽  
Laishram Chittaranjan Singh ◽  
L. Shaini Devi ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
High Sensitivity ◽  
Cross Sectional Study ◽  
Sepsis Management ◽  
Suspected Sepsis ◽  
Cross Sectional ◽  
Predictive Values ◽  
Procalcitonin Level

Background: The overlap of clinical presentation between sepsis and SIRS has presented diagnostic difficulty in these two close common scenarios. Blood culture remains the gold standard for detection of specific micro-organism, the non-availability of culture report early and difficulty in culture of many organisms is a challenge in differentiation of sepsis from its mimickers. Keeping this background, a simple tool is needed to guide sepsis management while awaiting the culture report. Therefore, the present study is conducted to determine the procalcitonin level in the setting suspicious of sepsis and to differentiate it from SIRS. The objective of the study was to determine Serum procalcitonin level as a biomarker of early diagnosis of sepsis and also to differentiate sepsis from severe inflammatory response syndrome.Methods: Cross sectional study on 79 patients aged above 18 years with suspected sepsis admitted in medicine ward. Serum procalcitonin levels were measured and analysed to look for any association with sepsis.Results: Culture was positive in 41 patients and 38 patients were procalcitonin positive. The sensitivity, specificity, positive predictive value and negative predictive values of serum procalcitonin were found as 96.68%, 94.74%, 95% and 92.30% respectively.Conclusions: There was a significant association between serum procalcitonin and culture positivity. Procalcitonin levels was found to have high sensitivity and high positive predictive value which may aid in the early diagnosis and guide initiation of anti-microbial therapy in sepsis.

scholarly journals Pre-Procedural Surveillance Testing for SARS-CoV-2 in an Asymptomatic Population Shows Low Rates of Positivity

2020 ◽  
Author(s):  
James A Mays ◽  
Alexander L Greninger ◽  
Keith R Jerome ◽  
John B Lynch ◽  
Patrick C Mathias
Keyword(s):  
Positive Predictive Value ◽  
Beck Depression Inventory ◽  
Predictive Value ◽  
Total Population ◽  
False Negative ◽  
Self Report ◽  
Case Group ◽  
Cross Sectional ◽  
Predictive Values ◽  
Sensitivity Specificity

Background: Depression is a common cause of mortality and morbidity worldwide. To detect depression, we compared Beck Depression Inventory scoring as a valid tool with participants self-reporting depression.Methodology: This cross-sectional study aimed to explore the diagnostic values of self-reporting in patients' with depression comparing to Beck Depression Inventory scoring in Mazandaran Persian cohort study, with a total of 1300 samples. The sample size was determined to include 155 participants through the census method. In order to increase the test power, 310 healthy participants were included in the study through random selection. In order to evaluate the diagnostic value of self-reporting, BDI-II was completed by blind interviewing to the case group as well as to another group who reported that they were not depressed, as control.Results: sensitivity, specificity, accuracy, false positive, false negative, positive and negative predictive values of self-reporting was calculated 58.4%, 79.1%,73.4%, 20.8%, 41.6%, 51.8%, and 83.2% for the total population respectively, as well as, sensitivity, specificity, accuracy, positive and negative predictive values of self-report in males were 83.3%, 77.2%, 77.1%, 43.8% and 95.6% and 53.7%, 78.1%, 71.2%, 49.2%, and 81.1% for females, respectively.Conclusion: The positive predictive value and sensitivity of self-reporting are insufficient in total population and females, and therefore self-reporting cannot detect depressed patients, but regarding to its average positive predictive value, perhaps, it can be used to identify non-depressant individuals.

scholarly journals HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

scholarly journals Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 503
Author(s):  
Thomas F. Monaghan ◽  
Syed N. Rahman ◽  
Christina W. Agudelo ◽  
Alan J. Wein ◽  
Jason M. Lazar ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Medical Literature ◽  
Disease Prevalence ◽  
Positive Test Result ◽  
Predictive Values ◽  
Sensitivity Specificity ◽  
Test Result ◽  
Test Probability

Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.

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