To describe the clinical impact of healthcare-associated (HA) respiratory syncytial virus (RSV) in hospitalized adults.Retrospective cohort study within a prospective, population-based, surveillance study of RSV-infected hospitalized adults during 3 respiratory seasons: October 2017–April 2018, October 2018–April 2019, and October 2019–March 2020.The study was conducted in 2 academically affiliated medical centers.Each HA-RSV patient  was matched  with 2 community-onset (CO) RSV patients (in whom RSV was detected ≤3 days of admission).Risk factors and outcomes were compared among HA-RSV versus CO-RSV patients using conditional logistic regression. Escalation of respiratory support associated with RSV detection (day 0) from day −2 to day +4 was explored among HA-RSV patients.In total, 84 HA-RSV patients were matched to 160 CO-RSV patients. In HA-RSV patients, chronic kidney disease was more common, while chronic respiratory conditions and obesity were less common. HA-RSV patients were not more likely to be admitted to an ICU or require mechanical ventilation, but they more often required a higher level of care at discharge compared with CO-RSV patients . Also, 29% of evaluable HA-RSV patients required respiratory support escalation; these patients were older and more likely to have respiratory comorbidities, to have been admitted to intensive care, and to die during hospitalization.HA-RSV in adults may be associated with escalation in respiratory support and an increased level of support in living situation at discharge. Infection prevention and control strategies and RSV vaccination of high-risk adults could mitigate the risk of HA-RSV. The healthcare costs of RSV-related hospitalizations in adults are similar to those of influenza. Although healthcare-associated (HA) influenza in hospitalized adults has been well described and is known to result in adverse clinical outcomes and increased healthcare utilization, less is known about HA-RSV. Although several studies have described RSV outbreaks in a variety of healthcare settings caring for adults, risk factors and outcomes associated with HA-RSV in nonoutbreak settings have not been well characterized.The impact of respiratory syncytial virus (RSV) in adults is increasingly appreciated. RSV can cause significant morbidity in older adults and in those who are immunocompromised or have cardiopulmonary comorbidities.To address this knowledge gap, we assessed the demographic characteristics, comorbid conditions, and clinical outcomes of adult patients with HA-RSV compared with adult patients hospitalized with community-onset (CO) RSV infections. As a potential surrogate for severity of illness in those with HA-RSV, we explored escalation of respiratory support associated with detection of RSV, and we compared the characteristics and outcomes of patients with HA-RSV who did and did not have escalation of respiratory support. As previously described, active surveillance for RSV took place during 3 successive RSV seasons from October 2017 to March 2020. To identify patients with laboratory-confirmed CO-RSV infection, study staff reviewed infection control databases and clinical virology laboratory logs to ascertain the results of PCR tests ordered as the standard of care for patients admitted with acute respiratory infection. To identify missed patients with CO-RSV and patients with HA-RSV infection, when the surveillance seasons ended, the electronic medical record (EMR) was queried for all positive RSV tests in hospitalized adults during the study period. The study sites were academically affiliated hospital systems in northern Manhattan and Rochester, New York. In the current retrospective study, we compared risk factors and outcomes for adult patients with HA-RSV with hospitalized adults with CO-RSV. The institutional review boards of the study sites approved this study with a waiver of informed consent.We designed a retrospective cohort study within a large, prospective, multicenter, multiseason, population-based, active surveillance study of RSV-associated hospitalization in adults 18 years of age and older. Eligible patients with CO-RSV were ≥18 years of age, had symptoms consistent with acute respiratory illness, and had RSV detected within 3 calendar days of admission. Each patient with HA-RSV was matched to 2 patients with CO-RSV by age (±5 years), sex, and RSV season. If >2 suitable CO-RSV patients were identified for an HA-RSV patient, those closest in age to the HA-RSV patient were selected.HA-RSV was defined as a patient hospitalized for 4 or more calendar days at a study site prior to RSV detection or transferred from an acute-care hospital with a combined length of hospital stay at either the outside hospital or study site of 4 or more contiguous days prior to RSV detection. Patients transferred from outside hospitals with known RSV infection were excluded. Patients with CO-RSV were selected from the previously described cohort of adults hospitalized with CO-RSV.Mycoplasma pneumoniae, Bordatella pertussis, and Chlamydophilia pneumoniae. The Rochester study sites used either the FilmArray Respiratory Panel, Simplexa FLU/RSV Duplex  or Cepheid GeneXpert Flu/RSV Duplex .The northern Manhattan study sites used the FilmArray Respiratory Panel , which detects influenza types A H3, A H1, B; parainfluenza virus types 1–4; RSV; human metapneumovirus; adenovirus, rhinovirus and enterovirus; and human coronavirus types 229E, HKU1, NL63, OC43; as well as International Classification of Disease, Tenth Revision (ICD-10) discharge codes that were related to RSV  were extracted from the EMR, and cause(s) of death were abstracted from the death certificate or death notes, when applicable.For HA-RSV patients, the reasons for hospitalization and for respiratory pathogen testing, such as worsening cough, were extracted from healthcare providers’ progress notes.  Patients transferred from acute-care hospitals or who were homeless were excluded from assessment of living situation at admission.For both HA-RSV and CO-RSV patients, demographic, and clinical characteristics, including comorbid conditions and living situation at admission, were collected. Living situation was classified as living independently at home, living at home with assistance from family members or home health aide or residing in an assisted living facility, or living in a rehabilitation or skilled nursing facility. Patients who died, were transferred to another acute-care hospital, or were not eligible for the analysis of living situation at admission were excluded from the analysis of changes in living situation. Readmission within 30 days of discharge was also assessed.For both HA-RSV and CO-RSV patients, outcomes included length of stay after RSV detection, admission to an intensive care unit (ICU) and/or mechanical ventilation initiated in the 4 days following RSV detection, and in-hospital mortality. For those who survived, living situation at discharge and changes in living situation from admission to discharge that reflected the need for increased support  were determined as previously described.2] and mean airway pressure [MAP] sustained for ≥1 hour each day) were collected before and after detection of RSV (day of detection = day 0). The daily maximum respiratory support used from day −10 (when available) to day −3 prior to RSV detection was considered the baseline support. This support was compared with the daily maximum respiratory support used from day −2 to day +4. This timeframe was selected because it reflected the potential time course of clinical deterioration from RSV prior to and after providers sent the diagnostic test. Patients who were transferred to the study sites from day −2 to day 0 were excluded from this analysis because a baseline period of support could not be reliably established.To explore escalation of respiratory support associated with HA-RSV, the type of respiratory support modalities  and degree of support  increase in supplemental oxygen by ≥ 1 liter per minute for ≥ 1 hour while maintaining the same mode of noninvasive respiratory support, (2) increase in modality of noninvasive support such as transition from room air to nasal cannula and/or nasal cannula to bilevel positive airway pressure (BiPAP), (3) transition from noninvasive to invasive support such as BiPAP to mechanical ventilation, or (4) increase in invasive support such as increase in MAP and/or FiOP < .10 in univariate analysis were then assessed in a multivariable logistic regression model after controlling for comorbid conditions that were significantly different between patients with HA-RSV and CO-RSV. Odds ratios and 95% confidence intervals were also calculated. Length of stay following RSV detection in patients with HA-RSV was compared with patients with CO-RSV but was not included in the bivariate or multivariable analysis due to confounding by comorbid conditions, particularly in patients with HA-RSV.Baseline demographic and clinical characteristics and outcomes of patients with HA-RSV versus CO-RSV were compared using conditional logistic regression. Outcomes with 2 test or the Fisher exact test, as appropriate, for categorical variables and the Mann-Whitney U test or the Student t test as appropriate for continuous variables. Outcomes with P < .10 in univariate analysis were then assessed in a multivariable logistic regression model. All analyses were conducted in SAS version 9.4 software  and P < .05 was considered statistically significant.The proportion of patients with HA-RSV with and without escalation of respiratory support was determined, and the types and timing of escalations were characterized. Clinical and demographic characteristics of patients with HA-RSV with and without escalation of respiratory support were compared using the χDuring the study period, 84 patients met the HA-RSV case definition including 33 patients between October 2017 and April 2018, 34 patients between October 2018 and April 2019, and 17 patients between October 2019 and March 2020  and were less likely to have respiratory comorbidities  and/or obesity .Of the 84 patients with HA-RSV, 76 were matched to 2 patients with CO-RSV and 8 were matched to 1 patient because an appropriate second CO-RSV match could not be found, for a total of 160 patients with CO-RSV. The demographic characteristics were similar between the 2 groups (Table P < .001). Although not statistically significant, the proportion of patients who died during hospitalization was higher among those with HA-RSV than those with CO-RSV  (Table P = .01). In multivariable analysis, after controlling for comorbid conditions, patients with HA-RSV remained more likely to require an increased level of support at discharge compared with patients with CO-RSV .Following RSV detection, the median length of hospitalization for patients with HA-RSV was longer than that for patients with CO-RSV  were changed from nasal cannula to a non-rebreather mask, 1 (5%) was changed from room air to BiPAP, 2 (9%) had mechanical ventilation initiated , and 2 (9%) had an increase in MAP and/or FiO2 on mechanical ventilation. Of 22 escalations, 15 (68%) occurred on day 0 or day +1  with HA-RSV were included in the analysis of escalation of respiratory support Table . From da% with HAP < .001) and were more likely to have respiratory comorbidities  (Table P < .001), to have had a longer length of stay following RSV detection , and to have died during hospitalization . In multivariable analysis, after controlling for age and respiratory conditions, ICU admission (P = .03) and mortality (P = .002) remained associated with escalation of respiratory support, but length of stay after RSV detection was no longer significantly different in those with and without escalation (P = .48).Patients with HA-RSV who had an escalation of respiratory support were significantly older than those without escalation  of 84 patients with HA-RSV with ICD-10 discharge codes available, 14 (18%) had an RSV-related diagnostic code. Only 1 of the 17 patients with an escalation of respiratory support had ICD-10 discharge codes available. Of the 13 patients with HA-RSV who died during admission, none had RSV listed as a cause or contributor to mortality on their death certificates. In contrast, patients with HA-RSV were less likely than patients with CO-RSV to have respiratory comorbidities.In this study, we assessed factors associated with HA-RSV in hospitalized adults in nonoutbreak settings, and we noted some interesting differences in patterns of comorbid conditions when we compared our study to similar studies conducted in HA versus CO influenza. Patients with HA influenza, compared with patients with CO influenza, had more chronic medical conditions, including higher rates of chronic lung disease other than asthma, cardiovascular diseases, metabolic disease, and immunosuppressive conditions. In the large prospective surveillance study from which the matched patients with CO-RSV were derived, we found that persons with chronic obstructive pulmonary disease and asthma had higher hospitalization rates due to CO-RSV infection than those without these conditions. In the current study, we noted that those with CO-RSV were more likely to be obese; however, previous studies have not identified obesity as a risk factor for severe RSV, including our previous finding that people with and without obesity had similar hospitalization rates for CO-RSV. The lack of association of obesity with severe RSV contrasts with the association of obesity with severe influenza and COVID-19. Finally, in the current study, patients with HA-RSV were more likely have CKD compared to patients with CO-RSV. Similarly, other studies have reported that patients with HA-influenza had significantly higher rates of renal disease compared to those with CO influenza. In a post hoc analysis, we found that the LOS for patients with HA-RSV and CO-RSV who had CKD was significantly longer than those without CKD: median, 10 days  versus 8 days , respectively (P = .004). Patients with CKD may have had an increased opportunity for exposure to respiratory viruses while hospitalized.Respiratory comorbidities are well known to be exacerbated by RSV infection, often leading to subsequent hospitalization.Although most patients with HA-RSV did not have decompensation in their respiratory status, 29% of patients with HA-RSV had an escalation of respiratory support from day −2 to day +4 from RSV detection, 15% required transfer to the ICU, and 4% had initiation of mechanical ventilation temporally associated with RSV detection. Those who had an escalation of respiratory support were older and more likely to have respiratory comorbidities, further underscoring the impact of RSV on older adults and those with chronic respiratory conditions. Those who had an escalation were also more likely to have severe outcomes, including ICU admissions and in-hospital mortality, although we were unable to determine whether these outcomes were attributed to RSV or to other underlying medical conditions. and they highlight the high level of healthcare utilization associated with HA-RSV and the need to prevent HA-RSV in hospitalized adults.Our findings contribute to an increased understanding of the impact of RSV in hospitalized adults. A substantial proportion of the patients with HA-RSV were frail on admission, evidenced by the finding that 14% lived in skilled nursing facilities prior to hospitalization. Furthermore, patients with HA-RSV were hospitalized for a median of 12 days prior to detection of RSV, suggesting that their admitting diagnoses and comorbid conditions required prolonged hospitalizations. Although ICU admission and mechanical ventilation after RSV detection were similar among patients with HA-RSV and CO-RSV, patients with HA-RSV had overall longer lengths of hospital stay following RSV detection, had a higher proportion of deaths, and were frailer at discharge. A higher proportion of patients in the HA-RSV group required admission to a skilled nursing facility. A combination of underlying conditions, HA-RSV infection, and deconditioning or loss of functional status associated with prolonged hospitalization in older adults could have contributed to these findings, Others have suggested that the use of death certificates alone may significantly underestimate mortality associated with RSV infection.Our data suggest that using ICD-10 codes or death certificates would underestimate the burden of HA-RSV in adults because few of the patients with HA-RSV had RSV-related ICD-10 codes and none had RSV noted on their death certificate. This finding could reflect the relative lack of clinical impact of HA-RSV and/or the timing of death relative to detection of RSV infection, but it may also reflect an underappreciation of the potential impact of HA-RSV by providers and diagnostic coders. Similarly, in a study of adults admitted with respiratory illness and RSV detected by PCR, only 51% of patients had an ICD-10 discharge code corresponding to RSV infection.This study had several limitations. Given the relatively small sample size, the study may have been underpowered to detect differences in some outcomes between patients with HA-RSV versus CO-RSV. The study was also conducted in 2 academic centers; thus, these findings may not be generalizable to other settings. Although EMRs were queried for all RSV-positive patients during the study seasons, patients with HA-RSV were likely underestimated because testing for RSV in hospitalized patients with new onset respiratory symptoms was not systematic. Furthermore, patients were not tested and documented to be negative at admission; thus, CO-RSV may have been misclassified as HA because of prolonged detection from an illness prior to admission. Another potential limitation was that criteria for escalation of respiratory support were not standardized but were implemented at the discretion of providers. Finally, clinical outcomes cannot definitively be attributed to RSV versus other underlying medical conditions.In conclusion, this study provides a unique perspective on the impact of HA-RSV in hospitalized adults. Healthcare-associated respiratory viruses are likely underappreciated in hospitalized adults. We found that HA-RSV was associated with escalation of respiratory support and an increased level of support in patients’ living situation at discharge. These outcomes increase the use of healthcare resources and related costs. Although this study cannot determine the mortality rate associated with HA-RSV, available data, such as death certificates and ICD-10 codes, likely underestimate this outcome. Infection control and prevention and RSV vaccines for adults at high risk of HA-RSV could mitigate this risk.


While the pledget suture technique has been the standard for surgical aortic. valve replacement (AVR), discussion continues regarding the possibility of the nonpledget suture technique to produce superior structural and hemodynamic parameters. This study aims to assess the effectiveness of the figure-of-eight suture technique in AVR, as determined by the incidence of prosthesis-patient mismatch (PPM).We reviewed records of patients (N = 629) who underwent a surgical AVR procedure between January 2011 and July 2018 at a single institution. Indexed effective orifice area values and PPM incidence were calculated from implanted valve size and patient body surface area. Incidence of none, moderate, and severe PPM was compared across AVR suture techniques.A total of 570 pledget and 59 figure-of-eight patients were compared for incidence of PPM. Patients who received AVR with the pledget suture technique had significantly lower echocardiographic measurements of baseline ejection fraction than patients who had received AVR with the figure-of-eight suture technique (p = 0.003). Patients who received the figure-of eight suture had a 14% decrease in moderate PPM compared to patients who received the pledget suture (p = 0.022). Patients who received the figure-of-eight suture also had a significantly higher rate of no PPM (p = 0.044).The use of the figure-of-eight suture technique in AVR can reduce the incidence of moderate PPM. While the pledget suture is the standard technique in AVR, the figure-of-eight suture technique may offer better structural and hemodynamic outcomes, especially for patients with a smaller aortic annulus. Aortic valve replacement (AVR) has evolved into the gold standard of treatment for severe aortic valve disease, with a 3.4% 30-day mortality rate and 3.2% rate of vascular complications . TraditiThe pledget and nonpledget suture techniques carry unique differences in design and utility that effect their clinical outcomes in AVR. In comparison to the noneverting pledget technique, Tabata and colleagues found that the simple interrupted suture without pledgets can significantly reduce PPM in small-valve AVR . Kim et From January 2011 to July 2018, 676 patients underwent a surgical AVR using either the pledgeted or figure-of-eight suture technique at The University of Chicago Medical Center. Of the original 676 patients, 629 (93%) met the requisite inclusion criteria. Patients who received a TAVR were excluded. Patients for whom a suture technique other than the pledgeted or figure-of-eight suture was used were excluded. There were three aortic valves for which manufacturer EOA was unavailable: the Edwards Magna sizes 27 and 29, and the St. Jude Trifecta size 19. Patients with unknown implanted valve size, valve manufacturer, manufacturer EOA, preoperative height or preoperative weight were excluded. This study was approved by the Institutional Review Board of The University of Chicago (IRB20-0360) on March 27, 2020. Individual patient consent was waived as the study design is retrospective and there was no interference with patient treatment.All patients underwent a standard AVR procedure through a complete median sternotomy. The existing aortic valve was de-calcified, and all debris was meticulously removed. In the pledgeted suture technique, 9 to 20 pledgeted mattress 2-0 Ti-Cron™ sutures  were placed around the circumference of the ventricular side of the annulus in a horizontal mattress fashion. Using two needles, all sutures were passed from the ventricular side of the annulus to the aortic side of the annulus and through the prosthetic valve sewing ring. In the figure-of-eight suture technique, 12 to 30 figure of eight 2-0 Ti-Cron™ sutures were used to secure the new aortic valve. One needle was repeatedly passed from the ventricular side of the annulus to the aortic side of the annulus and through the prosthetic valve sewing ring. The other needle remained on the ventricular side of the annulus, achieving semi-intraannular positioning. Both the figure-of-eight and pledget techniques are well visualized in Fig. Cardio Valve application , valve EOA was determined. The Cardio Valve application utilizes an algorithm derived from existing aortic prosthetic valve literature and valve manufacturer information to estimate EOA for a given valve type and size. Indexed effective orifice area (iEOA) was calculated by dividing the EOA from the Cardio Valve application by the preoperative patient body surface area (BSA). PPM was defined according to the iEOA value. PPM for an iEOA greater than 0.85 cm2/m2 was defined as “none”, for an iEOA between 0.65 and 0.85 cm2/m2 was defined as “moderate”, and for an iEOA of less than 0.65 cm2/m2 was defined as “severe”. Incidence of PPM was compared across AVR suture techniques , valve types , valve sizes , and valve manufacturers .Using the For the statistical analysis, two-sided p values of < 0.05 were considered statistically significant. Continuous data is reported as mean and standard deviation if normally distributed, or median and interquartile range (IQR) if skewed. Continuous variables were compared using Mann–Whitney U test. Categorical variables are expressed as number and percentage of patients and were compared using Fisher’s exact test.A total of 629 patients were studied for incidence of moderate and severe PPM. Among suture techniques, 570 patients (90.6%) underwent AVR with pledget sutures and fifty-nine patients (9.4%) underwent AVR with figure-of-eight sutures Table . Eighty-Demographic characteristics, medical history and other pre-, peri-, and post-operative patient data were compared between the pledgeted and figure-of-eight patients Table . There wPatients who received the figure-of-eight suture exhibited significantly lower rates of moderate PPM compared to patients who received pledget sutured  and large (25–29 mm) valve sizes . Dividing the patients into those with smaller and larger valve sizes reduced the sample size, lowering statistical power. However, a significant difference in moderate PPM was observed when the valve size groups were combined, likely due to the larger sample size and greater power. The small valve size group in particular did not reach statistical significance despite a large numerical difference in the proportion of patients with moderate PPM, possibly due to the small amount of figure-of-eight patients. Consideration of Simpson’s paradox due to the larger fraction of figure-of-eight patients who were of large valve size, which possessed a lower rate of PPM, are important, but cannot be resolved given the sample size limitations of the present study.We chose to evaluate PPM, as it is a well-known, modifiable predictor of post-operative hemodynamic abnormalities and all-cause mortality –24. ThisIn addition to PPM, several other complications can occur following AVR and increase mortality risk, including infectious endocarditis, chronic lung disease, and reduced left ventricular ejection fraction . FactorsTAVR has gained traction as a beneficial, minimally invasive procedure in patients who are deemed inoperable or who are opposed to surgical AVR. According to Puri et. al., TAVR grants equivalent clinical outcomes to surgical aortic valve replacement and maintains clinical superiority in patients with severe aortic stenosis . HoweverRegarding the limitations of our study, a relatively small cohort of patients at our center underwent AVR utilizing a figure-of-eight suture. This was due to the restricted, retrospective nature of our study and the general low incidence of figure-of-eight sutures utilized in AVR procedures. As a result, we did not divide our sample into smaller cohorts based on valve type and manufacturer; however, we employed logistic regression to confirm that these variables were not confounders. The pledget suture technique remained independently associated with moderate PPM after adjusting for valve type and manufacturer. Regarding suture technique, there was variability in the number of pledgeted sutures employed by each surgeon, and a lower number of pledget sutures may narrow the aortic root and result in PVLs. However, the majority of patients in the pledget cohort received 12–15 sutures (71.2%), whereas 18 patients received 9–11 sutures 3.2%) and 36 patients received 16–23 sutures (6.3%). Other surgeons did not comment on the number of pledgeted sutures utilized during their AVR operations (19.3%). We were unable to control for this variability due to the retrospective design of our study and differences in surgeon preference; however, a small subset (3.2%) of our patient population received below 12 pledget sutures, which could have caused a narrowing of the aortic root. We further did not associate suture technique with direct clinical outcomes such as paravalvular leaks and ejection fraction, as these comparisons have already been conducted in past literature [.2% and 3Suture technique can have a significant impact on PPM and its post-operative outcomes following AVR. Physicians operating AVR have mainly limited their approach to the simple interrupted suture technique or the interrupted suture with unabsorbable pledgets. We study a far-less utilized method, the figure-of-eight suture, and present it for the first time as a solution that can reduce moderate PPM following AVR. We also report that the figure-of-eight suture can yield significant rates of no PPM and offset adverse hemodynamic outcomes. Although the pledget suture has long been considered the standard technique in AVR, the figure-of-eight suture method may grant superior structural and hemodynamic results, especially in patients with a small aortic annulus who are at greater risk for such mismatch.


Evidence of the effectiveness of the WHO-recommended design of longer individualized regimens for multidrug- or rifampicin-resistant TB (MDR/RR-TB) is limited.To report end-of-treatment outcomes for MDR/RR-TB patients from a 2015–2018 multi-country cohort that received a regimen consistent with current 2022 WHO updated recommendations and describe the complexities of comparing regimens.We analyzed a subset of participants from the endTB Observational Study who initiated a longer MDR/RR-TB regimen that was consistent with subsequent 2022 WHO guidance on regimen design for longer treatments. We excluded individuals who received an injectable agent or who received fewer than four likely effective drugs.Of the 759 participants analyzed, 607  experienced successful end-of-treatment outcomes. The frequency of success was high across groups, whether stratified on number of Group A drugs or fluoroquinolone resistance, and ranged from 72.1% to 90.0%. Regimens were highly variable regarding composition and the duration of individual drugs.Longer, all-oral, individualized regimens that were consistent with 2022 WHO guidance on regimen design had high frequencies of treatment success. Heterogeneous regimen compositions and drug durations precluded meaningful comparisons. Future research should examine which combinations of drugs maximize safety/tolerability and effectiveness. Multidrug- or rifampicin-resistant TB (MDR/RR-TB) newly afflicts more than 500,000 people annually and has a global treatment success rate of 60%.The 2019 revisions to WHO treatment recommendations5The most recent update recommends three different short all-oral regimensHere, we report end-of-treatment outcomes for patients from a large multi-country 2015–2018 cohort who received an individualized, all-oral longer MDR/RR-TB regimen consistent with both 2019 and 2022 WHO recommendations.We conducted an analysis of data from the endTB Observational Study , implemented by national TB programmes (NTPs) with support from endTB consortium partners.10We included patients who received a first endTB treatment regimen for MDR/RR-TB between 1 April 2015 and 30 September 2018, provided informed consent for inclusion in the observational study, had documented rifampicin resistance and received regimens equivalent to what would be recommended in the latest 2022 WHO guidance.Collection of routinely captured clinical and laboratory data was organized by the three endTB consortium partners and standardized across all sites.13Longer (18–21 months) individualized treatment regimens were constructed by the local treating physician according to NTP guidelines and WHO 2016 Guidelines,We calculated treatment outcomes based on the WHO framework for outcome definitions.17th and 75th percentiles) of other key regimen characteristics, including the duration of the baseline regimen, the duration of each Group A drug included in the baseline regimen, and the number of drugs in the regimen likely and not likely to be effective. Analyses were conducted using SAS v9.4  and RStudio, PBC v1.4.1106 .We report the number and frequency of end-of-treatment outcomes across groups defined by the baseline FQ resistance and number of WHO Group A, B and C drugs in the initial baseline regimen. We also report the distribution . Participants provided written informed consent for inclusion in the observational cohort.A total of 2,789 patients consented to participate in the endTB observational study and initiated a BDQ-and/or DLM-containing regimen for the treatment of MDR/RR-TB. Of these, 759 met the inclusion criteria for these analyses. The median age at treatment initiation was 36.5 years (interquartile range [IQR] 28–49); 288 (37.9%) were women . ComorbiOverall, 80% (607/759) patients experienced a successful end-of-treatment outcome. Stratified by FQ DST results, treatment success was recorded in 77.6% (274/353) patients with FQ susceptibility, 85.8% (188/219) with FQ resistance and 77.7% (145/187) with unknown FQ susceptibility (th and 75th percentiles) after treatment initiation.While all patients received a WHO-conforming regimen, there was considerable variability in the composition of these regimens in terms of the number of unique regimens used within a category. For example, among individuals who initiated treatment with three Group A, at least one Group B and any number of Group C drugs, there are 24 regimens with unique drug combinations . RegimenThis report provides evidence on the performance of longer, all-oral treatment regimens conforming to 2019 and 2022 WHO DR-TB Guidelines.Due to the variability in regimen composition within categories, we chose not to directly compare the frequency of favorable outcomes for regimens having different numbers of Group A drugs. Therefore, the frequencies reported here do not account for differences in patient characteristics, such as disease severity, prior treatment with first- and second-line drugs, and comorbidities, such as HIV. While such comparisons are desirable, our findings regarding the heterogeneous nature of treatment highlight the complexities of comparative effectiveness analyses of longer individualized regimens for MDR/RR-TB. First, nearly all (80–90%) of patients had a change in their baseline regimen. Because regimens change over time, analyses that use baseline regimen classifications to estimate an intention-to-treat effect will not always adequately reflect relevant drug exposure throughout the course of treatment, or capture the reasons driving these changes, data that are essential to understanding regimen effectiveness.g-formula) that enable study of time-varying treatments, can address many of these challenges,A third challenge to comparative effectiveness analyses of longer regimens is the potential for heterogeneity in drug effectiveness within WHO groupings . Variability in contribution may be due to differences in anti-TB activity, lack of safety or tolerability, or interactions with other drugs in the regimen. Because regimens composed of the same number of drugs per group may have varying effectiveness, the most informative comparisons may be those that compare specific regimens rather than those characterized by the number of drugs per group. The WHO 2022 Guidelines take a step in this direction with multiple direct regimen comparisons.Limitations of the present analyses relate to the application of the 2019/2022 Guidelines to a cohort of patients whose treatment preceded these guidelines. First, the regimens were designed by clinicians in accordance with different guidanceTreatment regimens consistent in design with the WHO stepwise algorithm for MDR/RR-TB can achieve a frequency of treatment success surpassing 80%, when two or more Group A drugs are used in a four-drug regimen. Additional research is needed to determine the optimal drug combinations. Because the number of clinical trials with direct regimen comparisons is extremely limited, observational data will continue to play a key role in identifying the optimal treatment for MDR/RR-TB.


Chest computer tomography (CT) provides a readily available and efficient tool for COVID-19 diagnosis. Wavelet and contourlet transforms have the advantages of being localized in both space and time. In addition, multiresolution analysis allows for the separation of relevant image information in the different subbands. In the present study, transform-based features were investigated for COVID-19 classification using chest CT images. Several textural and statistical features were computed from the approximation and detail subbands in order to fully capture disease symptoms in the chest CT images. Initially, multiresolution analysis was performed considering three different wavelet and contourlet levels to determine the transform and decomposition level most suitable for feature extraction. Analysis showed that contourlet features computed from the first decomposition level (L1) led to the most reliable COVID-19 classification results. The complete feature vector was computed in less than 25 ms for a single image having of resolution 256 × 256 pixels. Next, particle swarm optimization (PSO) was implemented to find the best set of L1-Contourlet features for enhanced performance. Accuracy, sensitivity, specificity, precision, and F-score of a 100% were achieved by the reduced feature set using the support vector machine (SVM) classifier. The presented contourlet-based COVID-19 detection method was also shown to outperform several state-of-the-art deep learning approaches from literature. The present study demonstrates the reliability of transform-based features for COVID-19 detection with the advantage of reduced computational complexity. Transform-based features are thus suitable for integration within real-time automatic screening systems used for the initial screening of COVID-19. Coronavirus COVID-19) is a highly contagious, severe respiratory disease that spreads more rapidly than the traditional flu. On March 11, 2020, the World Health Organization (WHO) declared COVID-19 as a global pandemic . Since i is a higCOVID-19 symptoms include fever, dry cough, shortness of breath, chest pain, fatigue, loss of taste, loss of smell, headaches, sore throat, runny nose, diarrhea, abdominal pain, and nausea . Most COShortly after the start of the outbreak, polymerase chain reaction (PCR) tests became the gold standard for COVID-19 diagnosis. PCR works by detecting the genetic material of the virus on a swab taken from a person’s nose and/or throat. PCR tests require highly trained professionals and sophisticated lab equipment which can be a challenge in suburban or rural regions. In developing countries, PCR tests are also considerably expensive and not abundantly available. In addition, PCR tests suffer from low sensitivity, meaning that there is high probability that a COVID-19 positive case would be missed by the test .Chest computer tomography (CT) has thus rapidly emerged as a valuable tool for the diagnosis and follow-up of COVID-19 patients, especially those exhibiting acute respiratory symptoms. CT scan equipment are already readily available and commonly utilized in many medical institutions worldwide. In addition, CT scans were shown to have a higher sensitivity than PCR tests . HoweverCT image abnormalities in COVID-19 patients vary according to the disease stage and severity. Ground-glass opacites (GGO) are considered the earliest and most common radiological abnormality found in up to 98% of the chest CT scans of COVID-19 patients 12] compaMultiresolution analysis allows for the separation of an image’s high and low frequency information in its approximation and detail subbands, respectively. Features computed from the different subbands can thus thoroughly capture the changes in the chest CT images due to COVID-19 progression. In addition, contourlet transform provides a high degree of directionality which allows for robust representation of the image characteristics. Accordingly, the superiority of the proposed handcrafted L1-Contourlet features can be attributed to the relevance of the textural and statistical features computed from the different contourlet subbands. Deep pretrained networks commonly employed in medical applications are designed without consideration of the characteristics of the medical images or disease symptoms , 43. FeaAnother advantage of the introduced transform-based COVID-19 detection algorithm is the reduced computational complexity, specifically in comparison to the deep learning methods. L1-Contourelt features presented in this work were shown to require only a few milliseconds for feature extraction, and less than a second for training. On the other hand, deep networks typically require hours or even days for their training . For exaA contourlet-based COVID-19 detection algorithm from chest CT images was introduced and shown to outperform several state-of-the-art methods from literature. A comprehensive study was preliminary performed to determine the transform and decomposition level better suited for the required task. Experiments showed that the handcrafted features computed from the first contourlet decomposition level resulted in the most reliable performance. Feature computations required ~ 25 ms, whereas the rbf SVM classifier was trained in less than a second. Transform-based handcrafted features are thus highly efficient in comparison deep networks which typically require hours for their training. Feature selection was then performed to find the most relevant features, which were shown to include the three different feature types considered in this work. In the final experiments, accuracy, sensitivity, specificity, precision, and F-score of 100% were attained. The proposed contourlet-based approach was shown to outperform various state-of-the-art deep learning methods from literature by up to 11% indicating the superiority of the proposed method. Unlike generic pre-trained deep networks that do not consider specific disease symptoms in the analyzed medical images, handcrafted features have the advantage of efficiently capturing disease manifestations in the images which results in reliable performance. Future work involves designing a deep network that is specifically tailored to capture disease-related features within chest CT images, then comparing its performance to the transform-based method introduced in this study. Furthermore, it would be desirable for clinical purposes to determine the stage of COVID-19, i.e., mild, moderate, or progressive.


Little is known about the economic burden of treatment-resistant depression (TRD) in patients with physical conditions.To assess health care resource utilization (HRU) and costs, work loss days, and related costs in patients with TRD and physical conditions versus patients with the same conditions and non-TRD major depressive disorder (MDD) or without MDD.Adults aged < 65 years with MDD treated with antidepressants were identified in the OptumHealth Care Solutions database (July 2009-March 2017). Patients who received a diagnosis of MDD and initiated a third antidepressant regimen (index date) after 2 regimens of adequate dose and duration were defined as having TRD. Patients with non-TRD MDD and without MDD were assigned a random index date. Patients with < 6 months of continuous health plan eligibility pre- or post-index; a diagnosis of psychosis, schizophrenia, bipolar disorder/mania, dementia, and developmental disorders; and/or no baseline physical conditions  were excluded. Patients with TRD were matched 1:1 to each of the non-TRD MDD and non-MDD cohorts based on propensity scores. Per patient per year HRU, costs, and work loss outcomes were compared up to 24 months post-index date using negative binominal and ordinary least square regressions.P < 0.05). Health care costs were $22,541 in the TRD cohort, $17,450 in the non-TRD MDD cohort, and $10,047 in the non-MDD cohort, yielding cost differences of $5,091 (vs. non-TRD MDD) and $12,494 . In patients with work loss data available (n = 278/cohort), those with TRD had 2.0 and 2.9 times more work loss as well as $8,676 and $10,323 higher work loss costs relative to those with non-TRD MDD and without MDD, respectively .A total of 2,317 patients with TRD  had ≥ 1 co-occurring key physical condition . Relative to non-TRD MDD and non-MDD cohorts, respectively, patients with TRD had 46% and 235% more inpatient admissions, 28% and 128% more emergency department visits, and 53% and 155% more outpatient visits (all In patients with physical conditions, those with TRD had higher HRU and health care costs, work loss days, and associated costs compared with non-TRD MDD and non-MDD cohorts. What is already known about this subjectApproximately a third of patients with major depressive disorder (MDD), who receive pharmacologic treatment, have treatment-resistant depression (TRD). These patients incur significantly higher health care costs than those with treatment-responsive MDD  and without MDD.Patients with MDD and, in particular those with TRD, have higher rates of physical conditions than individuals without MDD.Despite the documented interplay between depression and physical health, limited data are available on the health care resource utilization (HRU) and health care costs of patients with TRD and co-occurring physical conditions.What this study addsPatients with TRD and concurrent physical conditions incurred higher all-cause HRU and higher all-cause health care costs compared with patients with the same physical conditions and non-TRD MDD or without MDD.Medical costs related to physical conditions were similar in patients with MDD regardless of TRD but were significantly higher in patients with TRD versus those without MDD. Thus, MDD may complicate the clinical management of physical conditions or have a negative impact on their course.The work loss-related costs of patients with TRD were more than 2-fold higher than those of patients with non-TRD MDD and 3-fold higher than those of patients without MDD.1 About 50% of individuals with MDD are reported to receive pharmacotherapy for their condition.2 Among them, approximately one third are estimated to have treatment-resistant depression (TRD),3 which is most commonly defined as an absence of response to ≥ 2 different antidepressant regimens of adequate dose and duration.4Major depressive disorder (MDD) is a common and incapacitating psychiatric condition with a 12-month prevalence of 7.2% in the United States in 2018.5 Medicare,6 Medicaid,8 and Veterans Health Administration.3 Specifically, commercially insured patients with TRD were reported to have $6,709 higher annual costs compared with patients with non-TRD MDD and $9,917 higher annual costs compared with individuals without MDD.5 On a national scale, patients with TRD are estimated to incur a direct health care costs burden of approximately $30 billion annually, representing more than 50% of the overall cost burden of pharmacologically treated MDD.3 Furthermore, patients with TRD were shown to exhibit greater work loss-related costs than matched controls with non-TRD MDD and without MDD.9The incremental economic burden of patients with TRD relative to patients with treatment-responsive MDD  or individuals without MDD is substantial across U.S. payers, including commercial health plans,11 Patients with MDD, and in particular those with TRD, have higher rates of comorbidities than individuals without MDD.12 Moreover, TRD appears to be associated with higher rates of cardiovascular disease,13 metabolic disorders,16 and respiratory disease13 relative to non-TRD MDD. Notably, the severity of depression has been shown to be associated with the presence of comorbidities,17 and previous studies found that co-occurring physical conditions are associated with a reduced likelihood of response to antidepressants.11Physical conditions often accompany mental illness. At least half of patients with MDD are affected by 1 or more co-occurring physical conditions.Given the interactions between depression and physical health, patients with TRD and co-occurring physical conditions may incur substantially increased health care utilization and higher health care costs. The present study sought to assess health care resource utilization (HRU) and costs, work loss days, and related costs among privately insured U.S. patients with TRD and key physical conditions compared with patients with the same physical conditions who either had non-TRD MDD or did not have MDD.18 the analysis of our study is exempt from institutional review for the following reasons: (a) it is a retrospective analysis of existing data (hence no patient intervention or interaction), and (b) no patient-identifiable information is included in the claims dataset.The Optum Health Care Solutions commercial insurance claims database  was used. This database contains information on 19.1 million privately insured individuals nationwide, including claimant demographics and insurance eligibility; medical and prescription drug claims; and short- and long-term disability claims, salary, and employment status for a subset of patients. This database only contains de-identified data that fully comply with the patient confidentiality requirements of the Health Insurance Portability and Accountability Act. Per Title 45 of Code of Federal Regulations, Part 46.101(b)(4),13 metabolic,16 and respiratory diseases13 or cancer13 (the TRD cohort) were matched and compared with 2 cohorts of patients with the same physical conditions who (a) had non-TRD MDD (the non-TRD MDD cohort) and (b) did not have MDD (the non-MDD cohort). The study period spanned from July 1, 2009, to March 31, 2017.A retrospective longitudinal matched cohort study design was used. Patients (between ages 18 and 64 years) with MDD and TRD and key physical conditions including cardiovascular,19 Adequate duration was defined as at least 6 weeks of continuous therapy with no gaps longer than 14 days. For patients in the non-TRD MDD cohort, the index date was imputed after the first antidepressant so that the distribution of time elapsed from the first antidepressant to the imputed index date would mimic that observed in the TRD cohort. For patients in the non-MDD cohort, the index date was imputed after the start of continuous insurance eligibility so that the distribution of time elapsed from the start of insurance eligibility to the imputed index date would mimic that observed in the TRD cohort.For patients in the TRD cohort, the index date was defined as the initiation of a third antidepressant treatment course after absence of response to 2 courses of adequate dose and duration within the same MDD episode. Adequate dose was defined as a starting dose recommended by the American Psychiatric Association treatment guidelines.Baseline characteristics  were evaluated in the 6-month period before the index date, which was defined as the baseline period. Study outcomes were evaluated during the follow-up period. The follow-up period spanned from the index date to the earliest among the following events: (a) the end of continuous eligibility or data availability , (b) 2 years post-index date, or (c) among the non-MDD cohort, a diagnosis of MDD or an antidepressant claim.International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]: 296.2x, 296.3x; International Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM]: F32.x [excluding F32.8], F33.x [excluding F33.8]) during the study period; (b) ≥ 1 antidepressant claim of adequate dose and duration after January 1, 2010; (c) no claims for an antidepressant in the 6 months before the first antidepressant claim; and (d) ≥ 1 diagnosis for depression  during the period from 6 months before the first antidepressant claim up to 2 years after the index date.Patients included in the TRD and non-TRD MDD cohorts met the following criteria: (a) ≥ 1 diagnosis of MDD  of adequate dose and duration within 2 years of the first antidepressant claim. An absence of response was defined as a switch of an antidepressant (< 180 days after the end of the previous treatment), an addition of an antidepressant, or an initiation of an augmentation therapy.Patients included in the non-MDD cohort had neither claims for an antidepressant nor diagnoses for MDD before the index date or within ≥ 6 months afterward.Finally, patients in all 3 cohorts met the following criteria: (a) no diagnosis for specific psychiatric conditions  during the study period; (b) ≥ 6 months of continuous insurance eligibility before and after the index date; (c) ≥ 1 claim for a key physical health condition  during the baseline period; and (d) aged 18-65 years at the index date.HRU and health care costs were reported by category, including inpatient, emergency department (ED), outpatient, and other services. Health care cost categories also included medical costs ; pharmacy costs; and total health care costs .Work loss and related costs were evaluated for employees with available work loss information and included paid disability and medical-related absenteeism. Paid disability days and associated costs were obtained from the data directly. Days of medical-related absenteeism were imputed as the length of stay of inpatient admissions, 1 day for each ED visit, or a half a day for each outpatient and other visit, plus days when the patient was eligible for disability, but not yet receiving benefits. Costs of medical-related absenteeism were obtained based on the number of days of medical-related absenteeism and wage data. All outcomes were reported per patient per year; in patients with less than a year of follow-up, outcomes were extrapolated for the full year based on the available data. Costs were expressed in 2017 U.S. dollars and measured from a payer’s perspective. Physical condition-related outcomes were defined based on claims with a diagnosis for any of the key physical conditions . Additionally, behavioral health-related outcomes were defined based on claims with an ICD-9-CM diagnosis between 290 and 319 (inclusive) or an ICD-10-CM diagnosis between F01 and F99 (inclusive).P values. Cost outcomes were reported as mean cost differences between matched cohorts, which were estimated using unadjusted ordinary least squares regressions; to account for a nonnormal distribution of cost data, 95% CIs and P values were obtained from nonparametric bootstrap procedures with 500 replications.Patients with TRD were matched 1:1 to patients with non-TRD MDD and without MDD based on (a) the availability of work loss data (exact match) and (b) age, sex, year of the index date, geographical region, type of health care plan, and presence of each physical condition (propensity score match). The balance of baseline characteristics between cohorts after matching was assessed with standardized differences . HRU and work loss outcomes during the follow-up period were compared between matched cohorts using unadjusted negative binomial regressions that accounted for the correlation between matched pairs. Results were reported as incidence rate ratios (IRRs) with 95% confidence intervals (CIs) and Of the 7,135 patients with TRD who met study selection criteria, 2,317 (32.5%) had ≥ 1 key co-occurring physical condition. Among 45,690 patients with non-TRD MDD who met study inclusion criteria, 13,003 (28.5%) had ≥ 1 key co-occurring physical condition. A total of 2,317 patients from the TRD cohort were matched 1:1 to patients with non-TRD MDD and without MDD.After matching, cohorts were well balanced in terms of demographic characteristics and key physical conditions . In the Given that cohorts were not matched on the presence of behavioral health conditions, anxiety and substance use disorder (SUD) were more common in the TRD cohort  than the non-TRD MDD  and non-MDD cohorts . Among patients with TRD, 69.0% used a selective serotonin reuptake inhibitor, 31.5% a norepinephrine-dopamine reuptake inhibitor, 30.6% a serotonin-norepinephrine reuptake inhibitor, 19.0% a serotonin modulator, 9.5% tricyclics or tetracyclics, and 8.1% norepinephrine-serotonin modulators during the baseline period. A total of 278 (12.0%) patients in each cohort had work loss data available and were included in the analysis of work loss outcomes. The mean duration of follow-up was 19.7 months in patients with TRD, 20.5 months in patients with non-TRD MDD, and 19.8 months in patients without MDD; the median was 24 months in all cohorts.P < 0.05; P < 0.01).Relative to patients with non-TRD MDD, those with TRD had higher all-cause HRU. Specifically, patients with TRD annually had 46% more inpatient admissions, 28% more ED visits, and 53% more outpatient visits than those with non-TRD MDD . The TRD cohort also had higher physical condition-related HRU in all categories, including 2.4 times more inpatient admissions, 2.5 times more inpatient days, 2.1 times more ED visits, 1.4 times more outpatient visits, and 2.4 times more other visits .When using the non-MDD cohort as comparator, the TRD cohort annually had 3.4 times more all-cause inpatient admissions, 3.6 times more all-cause inpatient days, 2.3 times more all-cause ED visits, 2.6 times more all-cause outpatient visits, and 2.5 times more all-cause other visits . This difference was predominantly driven by $2,423 higher outpatient and $2,295 higher pharmacy costs .Relative to patients with non-TRD MDD, patients with TRD incurred $5,091 higher all-cause health care costs annually (P < 0.001), a difference mainly driven by $4,280 higher outpatient costs, $3,605 higher pharmacy costs, and $3,462 higher inpatient costs , and about 9% due to higher physical condition-related medical costs.Relative to patients in the non-MDD cohort, those in the TRD cohort had $12,494 higher all-cause health care costs annually (P < 0.01), including 31% more medical-related absenteeism and 2.5 times more disability time than those with non-TRD MDD ; physical condition-related work loss was similar between both cohorts , including 2.2 times more medical-related absenteeism and 3.3 times more disability time ; physical condition-related work loss was similar between cohorts.Compared with patients without MDD, those with TRD incurred 2.9 times more all-cause work loss annually . Physical condition-related work loss costs were similar between cohorts . About 48% of all-cause work loss cost difference was due to higher behavioral health-related costs in the TRD cohort . Physical condition-related work loss costs were similar between these cohorts  may also contribute to them. Indeed, the index date of the TRD cohort corresponded to the initiation of a third antidepressant in the current study, whereas Amos et al. (2018) defined it as the initiation of the first antidepressant.5 The cost differences between the TRD and non-TRD MDD cohorts , as well as between the TRD versus non-MDD cohorts  were comparable in both studies.5 This suggests that the interaction of TRD and physical conditions might not substantially change the overall burden of TRD.Mean all-cause health care costs observed in the present study among patients with TRD were high. Specifically, these were about 30% higher than costs reported for the same cohorts in a previous study in commercially insured patients selected regardless of the presence of physical conditions.20 Moreover, the use of certain antidepressants or adjunctive treatments may play a role as well. Specifically, potential drug-drug interactions may hinder the optimal treatment of certain physical conditions. For safety reasons the use of some antidepressants requires additional monitoring in patients with MDD presenting with specific comorbidities,19 thereby also increasing HRU and costs. Further research is required to explore the relationship between TRD and the severity of physical conditions.The interplay between co-occurring physical conditions and MDD is complex and is not fully understood. Some indirect evidence from the present study suggest that TRD or MDD might negatively affect the course or management of preexisting physical conditions. Specifically, HRU and total health care costs related to physical conditions were higher among patients with TRD relative to patients without MDD. Although no statistically significant difference in health care costs related to physical conditions was found between the TRD and non-TRD MDD cohorts, patients with TRD had more ED visits due to physical conditions relative to patients with non-TRD MDD. MDD might complicate management of physical conditions or have a negative impact on their course through various channels. For example, patients with mental illness have been reported to have inadequate access to health care.11 Although this body of evidence suggests that physical conditions may in turn contribute to TRD, data from the current study are insufficient to further substantiate this hypothesis.Previous studies have shown that co-occurring physical conditions are associated with lower rates of response to antidepressants.With respect to the indirect economic burden among patients with physical conditions, patients with TRD in this study had all-cause work loss-related costs more than double those of patients with non-TRD MDD and triple those of patients without MDD. Work loss costs related to physical conditions were similar between the cohorts, and higher behavioral health-related costs in the TRD cohort explained just less than 50% of the difference in all-cause work loss-related costs between the cohorts. Interestingly, the incremental work loss-related costs of TRD were not dramatically different when using patients with non-TRD MDD or without MDD as a comparison basis. This seems to suggest that the majority of indirect cost burden related to work loss among medically treated patients with MDD is driven by TRD rather than by non-TRD MDD.Overall, the results from the current study provide evidence that TRD is associated with substantial economic burden in patients with physical conditions.4 there is no consensus definition endorsed by clinical guidelines.The present study is subject to several limitations. First, the algorithm used to identify patients with TRD relied solely on pharmacy claims, and clinical considerations to assess absence of response could not be incorporated. Although the most common definition of TRD was used ,Second, patients included in the non-TRD MDD cohort may have been in remission at the time of their randomly imputed index date, which may have impacted the magnitude of differences in HRU, costs, and work loss-related outcomes. Third, although exact and propensity score-based matching techniques were used to minimize the potential confounding, comparisons may be subject to residual biases due to unmeasured patient characteristics.Fourth, for patients with less than 1 year of follow-up, annual costs were extrapolated to 1 year based on available data. Fifth, available work loss-related data did not capture presenteeism. Sixth, results of this study might not be generalizable to patients covered by other types of insurance plans  or the uninsured; results in patients with work loss information available might not be generalizable to all patients with commercial insurance plans.Seventh, the requirement of at least 1 claim with a diagnosis for physical condition might have resulted in the inclusion of some patients with a rule-out diagnosis on a claim. In this case, the levels of reported HRU and costs in all cohorts might be underestimated. Lastly, as with all claims-based studies, analyses are subject to the possibility of inherent limitations related to inaccuracies due to coding errors and missing data.In patients with physical conditions, those who additionally had TRD incurred higher HRU and health care costs relative to patients with non-TRD MDD or without MDD. Moreover, patients with TRD incurred higher medical costs related to physical conditions compared with patients without MDD, which suggests that presence of TRD might complicate clinical management of physical conditions or have a negative impact on the course of the disease. TRD was also associated with substantially higher work loss and related costs. Notably, in the TRD cohort, work loss-related costs were more than 2-fold higher versus non-TRD MDD cohort and 3-fold higher versus non-MDD cohort.


The population health inpatient Medicare Advantage pharmacist (PHIMAP) intervention is a pharmacist-led, transitions-of-care intervention that aims to reduce hospital readmissions among Medicare Advantage beneficiaries. PHIMAP includes inpatient pharmacist participation in interdisciplinary rounds, admission and discharge medication reconciliation, pharmacy staff delivery of discharge medications to the bedside, personalized discharge medication lists and counseling, and communication with outpatient pharmacists through an electronic health record.To evaluate the effect of the PHIMAP intervention on unplanned 30-day same-hospital readmissions among Medicare Advantage patients.Those included were patients admitted to a large urban academic medical center between May 2018 and March 2020 who had a Medicare Advantage plan and were aged at least 18 years. A 2-group, quasi-experimental design was utilized. Control patients received the usual care, which included a best possible medication history and a postdischarge phone call. A multivariable logistic regression model was estimated to predict unplanned 30-day same-hospital readmissions. This study was a Hypothesis Evaluating Treatment Effectiveness study.In total, 884 patients were included. The majority were White (59.0%), non-Hispanic (87.7%), English speaking (90.5%), and older adults . We detected no statistically significant association between the PHIMAP intervention and unplanned 30-day same-hospital readmissions . After adjusting for patient demographics and clinical covariates, significant predictors of 30-day readmissions included the number of emergency department/inpatient visits within 180 days prior to index admission ; discharge to a post–acute care facility, such as an inpatient rehabilitation facility, long-term acute care facility, or skilled nursing facility ; hospital length of stay in days ; and the Agency for Healthcare Research and Quality Elixhauser Comorbidity Index score .Significant predictors of readmissions among Medicare Advantage beneficiaries were consistent with greater illness severity, including a recent history of prior hospital utilization, a discharge to post–acute care facility (vs home), a longer length of hospital stay, and a higher comorbidity burden. Although we detected no statistically significant association between PHIMAP and unplanned 30-day same-hospital readmissions, differences in study group assignment based on the day of hospital discharge (weekend vs weekday) was a noted limitation of this study. Future studies of inpatient pharmacist-led interventions should plan to minimize the risk of selection bias due to differences in the time of patient discharge. This study looked at pharmacist care around hospital discharge for patients with Medicare Advantage insurance. We compared patients who got pharmacist care with those who did not get it. This study found that pharmacist care had no effect on readmissions. The differences between groups were a limitation in this study. Implications for managed care pharmacyThis study evaluated the effect of a multicomponent intervention delivered by a transitions-of-care pharmacist prior to hospital discharge in addition to standard care, which included a pharmacist-led, postdischarge follow-up phone call. We detected no significant difference in unplanned 30-day same-hospital readmissions between the intervention and standard care. Future studies seeking to evaluate inpatient pharmacist-led interventions should be designed to minimize the risk of selection bias, including differential assignment to study groups based on the day of hospital discharge.1 Approximately 34% of the Medicare population is enrolled in an MA plan.1 A 2019 study of a nationally representative sample of MA beneficiaries showed that MA beneficiaries were more likely to be readmitted within 30 days of hospital discharge compared with traditional Medicare fee-for-service beneficiaries.2 Of note, the study adjusted for readmission risk attributable to patient age, sex, race, ethnicity, income, education, comorbidity burden, and dual Medicare/Medicaid coverage.2 Compared with fee-for-service plans, one reason that MA beneficiaries experience higher risk-adjusted readmission rates may be due to more frequent prescription of high-risk medications associated with adverse drug events (ADEs).3 Approximately 17%-21% of MA beneficiaries are prescribed at least one high-risk medication associated with ADEs that increase the risk of readmission.4 Given the MA population’s higher risk-adjusted readmission rate and use of high-risk medications as a contributing factor to readmissions, interventions are needed to reduce medication-related hospital readmissions that affect the MA population.3In the United States, Medicare Advantage (MA) insurance plans are privately managed and capitated health plans that offer an alternative to the Medicare fee-for-service plan managed by the federal government, also known as traditional Medicare. The Medicare population includes older adults (aged 65 years and older) as well as younger adults with disabilities, end-stage renal disease, and amyotrophic lateral sclerosis.5,6 Yet, the majority of 30-day readmissions due to ADEs are preventable.7 Previous studies have shown that pharmacist-led interventions can reduce risk factors for medication-related hospital readmissions in the MA population, including high-risk medication use,8 medication-related problems,10 poor medication adherence,12 and inappropriate prescribing practices for at-risk subgroups, such as patients with diabetes.13 Prior studies have also investigated the effect of pharmacist-led interventions delivered during home visits to improve transitions of care and to reduce 30-day readmissions in the MA population, although the results have been mixed.15Medication-related hospital readmissions are common, costly, and more prevalent among older patients.16 Therefore, this study seeks to contribute to the transitions-of-care literature by exploring the potential benefits of adding pharmacist-led transitions-of-care interventions prior to hospital discharge  in addition to standard care that included a pharmacist-led postdischarge follow-up phone call.The objective of this study was to evaluate the effect of the population health inpatient MA pharmacist (PHIMAP) intervention on unplanned 30-day readmissions among MA beneficiaries. This study took advantage of a unique context in which pharmacist-led postdischarge follow-up phone calls had already been implemented as standard care in the study setting for MA patients. Pharmacist-led postdischarge follow-up phone calls have been shown to reduce preventable ADEs within 30 days of hospital discharge.17 In addition to medication safety, the ITC framework includes 9 other domains associated with readmission reduction: (1) the complete communication of information; (2) the availability, timeliness, clarity, and organization of information; (3) the education of patients to promote self-management; (4) the monitoring and managing of symptoms after discharge; (5) the help of social and community supports; (6) advanced care planning; (7) the coordination of care among team members; (8) discharge planning; and (9) follow-up with outpatient providers.18 In this Hypothesis Evaluating Treatment Effectiveness study, we hypothesized that the PHIMAP intervention would reduce 30-day readmissions among MA beneficiaries.1920 An institutional review board (IRB) application was submitted to the IRB at Cedars-Sinai Medical Center in Los Angeles, California. The study received exemption from full IRB review as a quality improvement initiative. IRB staff reviewed and approved all procedures for retrospective data collection, management, and analyses.The Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) were used to guide the reporting of this work.21 Housed under the medical center’s organizational umbrella, the Cedars-Sinai Medical Delivery Network includes an integrated network of over 300 physicians providing inpatient and outpatient care to over 100,000 patients per year, as well as over 400 physicians in private practice serving the Los Angeles area.The quality improvement initiative was undertaken at a single, large, urban academic medical center: Cedars-Sinai Medical Center . The medical center includes 886 licensed beds and more than 4,500 physicians and nurses on staff. In the 2019-2020 fiscal year, the medical center serviced 51,000 admissions, 119,000 emergency department visits, and 1,290,000 outpatient visits.The study utilized a 2-group, quasi-experimental design . The stuAll control patients received the usual care. Prior to the implementation of PHIMAP, the usual care for MA patients at the study site included the following inpatient pharmacist-led interventions: (1) the best possible medication history conducted by a pharmacy technician or pharmacist for high-risk patients taking at least 10 prescription medications and (2) a postdischarge follow-up phone call made by outpatient pharmacists.In addition to the usual care, PHIMAP added the following pharmacist-led intervention activities:1.The best possible medication history conducted by a pharmacy technician or pharmacist for all MA patients receiving the PHIMAP intervention, regardless of the number of prescription medications taken prior to admission2.Inpatient pharmacist participation in daily interdisciplinary rounds3.Predischarge medication reconciliation4.Pharmacist-facilitated delivery of discharge medications to the patient’s bedside (in place of nurse facilitation)5.The provision of a personalized discharge medication list prior to discharge6.Discharge medication counseling7.A discharge summary note sent to the patient’s outpatient pharmacist through an electronic health record (EHR) (Epic Systems Corporation).One full-time inpatient pharmacist, who had previously completed 1 year of residency training in transitions-of-care pharmacy, delivered the PHIMAP intervention on weekdays (Mondays through Fridays from 9:00 AM to 5:30 PM) starting in November 2018. Patients were assigned to the intervention group using a convenience sampling approach whereby patients received PHIMAP to the extent that the pharmacist had time available to deliver the intervention. Patients who met all inclusion criteria but did not receive the intervention were assigned to the control group.The primary outcome of the study was unplanned 30-day same-hospital readmissions, operationally defined as readmission to the same hospital on an emergent or urgent basis occurring within 30 days after hospital discharge. The rationale for selecting the primary outcome was that a change in unplanned 30-day readmissions would be a strong motivator for hospital and health system leaders to financially invest in the intervention for long-term maintenance and sustainability. Hospital pharmacy leaders and pharmacists involved in the intervention’s development, delivery, and administrative oversight were included on the study team and corroborated the rationale for primary outcome selection.22 Definitions of each hospital admission category follow the National Uniform Billing Committee’s UB-04 Data Specifications Manual.23Readmissions on an elective basis were excluded using an indicator variable obtained from the EHR that categorizes each hospital admission as emergent, urgent, or elective. The indicator variable corresponds to inpatient data reporting requirements for the State of California’s Department of Health Care Access and Information, which routinely makes aggregated hospital data available to the public.24 number of outpatient medications prior to index hospital admission, number of same-hospital emergency department/inpatient visits within 180 days prior to index admission, length of hospital stay, discharge location , and day of discharge (weekday or weekend). Data on patient demographics and clinical covariates were obtained retrospectively from the EHR. The potential for multicollinearity among predictor variables was examined through visual inspection of the covariance matrix and subsequent evaluation of formally derived measures . Statistical modeling and model evaluation were performed using SAS version 9.4 (SAS Institute Inc.).A multivariable logistic regression model was estimated to predict the binary outcome (yes/no) of unplanned 30-day same-hospital readmission. Predictor variables included study group (intervention or control); patient demographics, including age, sex, race, ethnicity, marital status, and language (English or non–English speaker); and clinical covariates, including the Agency for Healthcare Research and Quality Elixhauser Comorbidity Index (weighted for risk of readmission due to comorbidity burden with weighted scores ranging from −4 [lesser disease burden] to 229 [greater disease burden]),P values adjusted for multiple comparisons using the Holm sequential procedure.25 Descriptive statistics and between-group comparisons were performed using SPSS Statistics version 24.0 (IBM).Descriptive statistics and between-group comparisons of demographics and clinical covariates were obtained for the following groups: (1) intervention group; (2) preperiod controls, defined by an index hospital admission before PHIMAP implementation between May 2018 and October 2018; and (3) postperiod controls, defined by an index hospital admission after PHIMAP implementation between November 2018 and March 2020. Pearson chi-square tests of independence were used to compare groups on nominal categorical data, with post hoc pairwise z-test comparisons. Krusal-Wallis tests corrected for tied ranks were used to compare group medians, with post hoc Mann-Whitney U-tests for pairwise differences. The α level of significance for statistical testing was set at 0.05 with two-sided Missing data were observed for race and ethnicity. In total, 6 patients (0.7%) had an unknown race, and 6 patients (0.7%) had an unknown ethnicity. One patient had both an unknown race and ethnicity (0.1%). In preparation for multivariable modeling, missing data were input using the most frequent categorical value (mode imputation) and assumed to be missing at random.P > 0.05). In contrast, study groups were significantly different in the following ways. Compared with the postperiod controls, patients in the intervention group had a higher comorbidity burden ; more emergency department/inpatient visits within 180 days prior to index admission ; and a longer length of hospital stay . Furthermore, compared with postperiod controls, preperiod controls had a higher comorbidity burden .After adjusting for multiple comparisons, patients in the preperiod control, postperiod control, and intervention groups did not differ significantly with respect to age, sex, race, ethnicity, language, marital status, number of outpatient medications, length of stay, or discharge location (Holm-adjusted P < 0.05). Preperiod controls were more likely to have been discharged on a weekday compared with postperiod controls (adjusted P < 0.05).Consistent with the intervention having been administered on weekdays, patients in the intervention group were nearly always discharged on a weekday (97.2%) and were more likely to be discharged on a weekday compared with either the preperiod controls (75.4%) or postperiod controls . However, patients discharged on weekdays had a significantly higher comorbidity burden  and more emergency department/inpatient visits within 180 days prior to index admission .P > 0.05; P > 0.05; Unadjusted 30-day same-hospital readmission rates were not significantly different between the intervention group (14.3%), preperiod controls (13.1%), or postperiod controls (9.6%) (P > 0.05), indicating a well-fitting model.26 Second- and third-order interaction terms between study group, time period (preperiod vs postperiod), and day of discharge (weekday vs weekend) were tested in the model. All interaction terms were found to be nonsignificant (P > 0.05) and dropped from the final model with no change in the significance or direction of model main effects.In multivariable modeling, an examination of the covariance matrix showed no evidence of severe collinearity between predictor variables . Hosmer and Lemeshow’s goodness-of-fit test was nonsignificant (P < 0.01); discharge to a post–acute care facility ; length of stay in days ; and comorbidity burden measured using the Agency for Healthcare Research and Quality Elixhauser Index score . After standardizing the regression coefficients, the Elixhauser Index score that indicates comorbidity burden was the strongest predictor of 30-day readmissions in this sample of MA patients  comorbidity burden, (2) prior hospital utilization, (3) length of stay, and (4) discharge to institutional settings, which are each indicative of greater illness severity.We note that the following contextual elements may have interacted with the PHIMAP intervention and contributed to the intervention’s insignificant effect on 30-day hospital readmissions. Patients in the control group notably received pharmacist-led interventions as part of the existing standard of care at the study site, including a best possible medication history and postdischarge follow-up phone call, which may have reduced the differences in readmission risk between the study groups. Further, the intervention was delivered solely on weekdays when pharmacists with transitions-of-care training were staffed at the study site. Consequently, patients who were discharged on the weekend were less likely to receive the intervention. We observed that patients assigned to the control group after PHIMAP implementation  were more likely to have been discharged on a weekend, had a lower comorbidity burden, and less prior hospital utilization than the intervention group, suggesting that the postperiod controls had a better health status on average and a lower readmission risk.P = 0.04). After adjusting for multiple comparisons, patient demographics, and clinical characteristics, however, the day of discharge was not a significant predictor of readmissions. This observation contrasts with the meta-analysis of Chiu et al (2020), which found that risk-adjusted 30-day readmission rates were higher among patients discharged from US hospitals on weekends compared with weekdays, though high heterogeneity was observed among studies included in the meta-analysis.32 Future studies of managed care pharmacy interventions that aim to reduce hospital readmissions for diverse patient populations should be carefully designed to minimize the potential impact of a “weekend effect” on differences in readmission rates between study groups.Differences in hospital care on the weekend may have also been a confounding factor. We found that unadjusted 30-day readmission rates were overall lower in patients who were discharged on weekends compared with patients who were discharged on weekdays , who is the study pharmacist who delivered the intervention, manually reviewed all patients’ EHRs to confirm that group assignments were correctly labeled. This review for data quality assurance was completed prior to data analysis, which was performed independently by another author (A.T.N.). Additionally, because this study was being pursued following a quality improvement initiative that had previously been completed, we were unable to assess intervention fidelity or whether deviations from the intended intervention had occurred.35Factors limiting external validity include the use of a single-site design, which limited generalizability to other settings with different characteristics. We were unable to include readmissions to other hospitals  in our measurement of 30-day readmissions. Further, we did not measure other potentially important process outcomes or secondary outcomes, such as changes in high-risk medication use, inappropriate prescribing practices, medication adherence, medication errors, ADEs, or mortality. These may be more important indicators of the quality of care compared with readmissions, which is supported in part by the observation that reductions in hospital readmission rates have been associated with concerning increases in patient mortality rates.This study evaluated the effect of a multicomponent, transitions-of-care pharmacist intervention on unplanned 30-day same-hospital readmissions among MA beneficiaries. Although we confirmed known associations between illness severity and likelihood of readmission, we observed no significant effect of the PHIMAP intervention on 30-day readmissions. However, differences in the likelihood of study group assignment based on the day of hospital discharge (weekend vs weekday) may have introduced bias. Future studies seeking to evaluate the effects of managed care pharmacy interventions on hospital readmissions should be designed to minimize the risk of selection bias due to differences in the day of discharge.