Comparative Value of Four Measures of Retention in Expert Care in Predicting Clinical Outcomes and Health Care Utilization in HIV Patients

This study compared the ability of four measures of patient retention in HIV expert care to predict clinical outcomes. This retrospective study examined Veterans Health Administration (VHA) beneficiaries with HIV (ICD-9-CM codes 042 or V08) receiving expert care (defined as HIV-1 RNA viral load and CD4 cell count tests occurring within one week of each other) at VHA facilities from October 1, 2006, to September 30, 2008. Patients were ≥18 years old and continuous VHA users for at least 24 months after entry into expert care. Retention measures included: Annual Appointments (≥2 appointments annually at least 60 days apart), Missed Appointments (missed ≥25% of appointments), Infrequent Appointments (>6 months without an appointment), and Missed or Infrequent Appointments (missed ≥25% of appointments or >6 months without an appointment). Multivariable nominal logistic regression models were used to determine associations between retention measures and outcomes. Overall, 8,845 patients met study criteria. At baseline, 64% of patients were virologically suppressed and 37% had a CD4 cell count >500 cells/mm3. At 24 months, 82% were virologically suppressed and 46% had a CD4 cell count >500 cells/mm3. During follow-up, 13% progressed to AIDS, 48% visited the emergency department (ED), 28% were hospitalized, and 0.3% died. All four retention measures were associated with virologic suppression and antiretroviral therapy initiation at 24 months follow-up. Annual Appointments correlated positively with CD4 cell count >500 cells/mm3. Missed Appointments was predictive of all primary and secondary outcomes, including CD4 cell count ≤500 cells/mm3, progression to AIDS, ED visit, and hospitalization. Missed Appointments was the only measure to predict all primary and secondary outcomes. This finding could be useful to health care providers and public health organizations as they seek ways to optimize the health of HIV patients

Use of DNA Sequencing Analysis To Confirm Fungemia Due to Trichosporon dermatis in a Pediatric Patient

This is the first reported case of human disease caused by Tricosporon dermatis, an organism recently transferred to the genus Trichosporon from Cryptococcus and now confirmed to be a human pathogen

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Abstract Background Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. Methods Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. Results Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83–0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89–0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71–0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state. Conclusion CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 201

Durvalumab Treatment Patterns for Patients with Unresectable Stage III Non-Small Cell Lung Cancer in the Veterans Health Administration (VHA): A Nationwide, Real-World Study

Background: Durvalumab is approved for the treatment of adults with unresectable stage III non-small cell lung cancer (NSCLC) post-chemoradiotherapy (CRT). This real-world study describes patient characteristics and durvalumab treatment patterns (number of doses and therapy duration; treatment initiation delays, interruptions, discontinuations, and associated reasons) among VHA-treated patients. Methods: This was a retrospective cohort study of adults with unresectable stage III NSCLC receiving durvalumab at the VHA between 1 January 2017 and 30 June 2020. Patient characteristics and treatment patterns were presented descriptively. Results: A total of 935 patients were included (median age: 69 years; 95% males; 21% Blacks; 46% current smokers; 16% ECOG performance scores ≥ 2; 50% squamous histology). Durvalumab initiation was delayed in 39% of patients (n = 367). Among the 200 patients with recorded reasons, delays were mainly due to physician preference (20%) and CRT toxicity (11%). Overall, patients received a median (interquartile range) of 16 (7–24) doses of durvalumab over 9.0 (2.9–11.8) months. Treatment interruptions were experienced by 19% of patients (n = 180), with toxicity (7.8%) and social reasons (2.6%) being the most cited reasons. Early discontinuation occurred in 59% of patients (n = 551), largely due to disease progression (24.2%) and toxicity (18.2%). Conclusions: These real-world analyses corroborate PACIFIC study results in terms of the main reasons for treatment discontinuation in a VHA population with worse prognostic factors, including older age, predominantly male sex, and poorer performance score. One of the main reasons for durvalumab initiation delays, treatment interruptions, or discontinuations was due to toxicities. Patients could benefit from improved strategies to prevent, identify, and manage CRT and durvalumab toxicities timely and effectively

Patient outcomes at 24 months.

<p>*Patients who had these characteristics at baseline were excluded from these results.</p><p>ED = emergency department; ART = antiretroviral therapy; AIDS = acquired immune deficiency syndrome; SD = standard deviation</p><p>Patient outcomes at 24 months.</p

Multivariable nominal logistic regression models for patient outcomes at 24 months: OR (p-value), n = 8,845^a^,^b^,^c.

<p>^aCovariates were: patient age, sex, race, marital status, priority group, baseline viral load, baseline CD4 count, Charlson comorbidity score, and treatment facility.</p><p>^bPatients with outcome of interest at baseline were excluded from these analyses</p><p>^cBold indicates statistical significance</p><p>AIDS = progression to acquired immune deficiency syndrome; ED = emergency department; ART = antiretroviral therapy initiation</p><p>Multivariable nominal logistic regression models for patient outcomes at 24 months: OR (p-value), n = 8,845<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120953#t004fn001" target="_blank">^a</a>^,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120953#t004fn002" target="_blank">^b</a>^,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120953#t004fn003" target="_blank">^c</a>.</p

Retention measures at 24 months.

<p>Retention measures at 24 months.</p

Patient baseline characteristics.

<p>AIDS = acquired immune deficiency syndrome; HIV = human immunodeficiency syndrome; SD = standard deviation</p><p>Note: Priority 1 veterans are 50%–100% disabled by a military service-related condition and have no co-pays for care or prescription medications; Priority 2–6 veterans have medication co-pays; Priority 7–8 veterans have agreed to pay co-pays for care and medications and are ineligible under Priorities 1–6.</p><p>Note: Charlson score defines HIV/AIDS using ICD-9-CM code 042 and does not include asymptomatic HIV infection (ICD-9-CM code V08)</p><p>Patient baseline characteristics.</p

Comparative Value of Four Measures of Retention in Expert Care in Predicting Clinical Outcomes and Health Care Utilization in HIV Patients

This study compared the ability of four measures of patient retention in HIV expert care to predict clinical outcomes. This retrospective study examined Veterans Health Administration (VHA) beneficiaries with HIV (ICD-9-CM codes 042 or V08) receiving expert care (defined as HIV-1 RNA viral load and CD4 cell count tests occurring within one week of each other) at VHA facilities from October 1, 2006, to September 30, 2008. Patients were ≥18 years old and continuous VHA users for at least 24 months after entry into expert care. Retention measures included: Annual Appointments (≥2 appointments annually at least 60 days apart), Missed Appointments (missed ≥25% of appointments), Infrequent Appointments (>6 months without an appointment), and Missed or Infrequent Appointments (missed ≥25% of appointments or >6 months without an appointment). Multivariable nominal logistic regression models were used to determine associations between retention measures and outcomes. Overall, 8,845 patients met study criteria. At baseline, 64% of patients were virologically suppressed and 37% had a CD4 cell count >500 cells/mm3. At 24 months, 82% were virologically suppressed and 46% had a CD4 cell count >500 cells/mm3. During follow-up, 13% progressed to AIDS, 48% visited the emergency department (ED), 28% were hospitalized, and 0.3% died. All four retention measures were associated with virologic suppression and antiretroviral therapy initiation at 24 months follow-up. Annual Appointments correlated positively with CD4 cell count >500 cells/mm3. Missed Appointments was predictive of all primary and secondary outcomes, including CD4 cell count ≤500 cells/mm3, progression to AIDS, ED visit, and hospitalization. Missed Appointments was the only measure to predict all primary and secondary outcomes. This finding could be useful to health care providers and public health organizations as they seek ways to optimize the health of HIV patients