Patients and Methods: Patients undergoing major open abdominal surgery were monitored continuously with FloTrac® to measure SVV and CI along with standard monitoring. Both SVV and CI were noted at baseline and every 10 min thereafter till the end of surgery and were observed for concurrence between the measurements.
Results: 1800 pairs of measurement of SVV and CI were obtained from 60 patients. Mean SVV and CI (of all patients) measured at different time points of measurement showed that as SVV increased with time, the CI dropped correspondingly. When individual readings of CI and SVV were plotted against each other, the scatter was found to be wide, reiterating the lack of agreement between the two parameters (R2 = 0.035). SVV >13% suggesting hypovolemia was found at 207 time points. Of these, 175 had a CI >2.5 L/min/m2 and only 32 patients had a CI <2.5 L/min/m2.
Conclusion: SVV, a dynamic index of fluid responsiveness can be used to monitor patients expected to have large fluid shifts during major abdominal surgery. It is very specific and has a high negative predictive value. When SVV increases, CI is usually maintained. Since many factors affect SVV and CI, any increase in SVV >13%, must be correlated with other parameters before administration of the fluid challenge.
MATERIALS AND METHODS: In this multicenter cross-sectional study, data on mechanical ventilation and clinical outcomes were collected. Predictors of mortality were analyzed by univariate and multivariable logistic regression. A scoring system was generated to predict 28-day mortality.
RESULTS: A total of 1408 patients were enrolled. In 138 patients with acute respiratory distress syndrome (ARDS), 65.9% were on a tidal volume ≤ 8 ml/kg predicted body weight (PBW), and 71.3% were on sufficient PEEP. In 1270 patients without ARDS, 88.8% were on a tidal volume ≤ 10 ml/kg PBW. A plateau pressure
AREAS COVERED: This paper discusses the complex process of ECM regulation in TM and explores promising therapeutic targets. The role of Transforming Growth Factor-β as a central player in ECM deposition in TM is discussed. We elaborate the key regulatory processes involved in its activation, release, signaling, and cross talk with other signaling pathways including Rho GTPase, Wnt, integrin, cytokines, and renin-angiotensin-aldosterone. Further, we summarize the therapeutic agents that have been explored to target ECM dysregulation in TM.
EXPERT OPINION: Targeting molecular pathways to reduce ECM deposition and/or enhance its degradation are of considerable significance for IOP lowering. Challenges lie in pinpointing specific targets and designing drug delivery systems to precisely interact with pathologically active/inactive signaling. Recent advances in monoclonal antibodies, fusion molecules, and vectored nanotechnology offer potential solutions.
METHODS: This cross-sectional study included 390 participants from a primary care clinic in Selangor, Malaysia, between February and June 2022. The inclusion criteria were high-CV risk individuals, that is, Framingham risk score >20%, diabetes without target organ damage, stage 3 kidney disease, and very high levels of low-density lipoprotein cholesterol (LDL-C) >4.9 mmol/L or blood pressure (BP) >180/110 mmHg. Individuals with existing CVD were excluded. The treatment targets were BP <140/90 mmHg (≤135/75 for diabetics), LDL-C <2.6 mmol/L, and HbA1c ≤6.5%. Multiple logistic regressions determined the association between sociodemographic, clinical characteristics, health literacy, and medication adherence with the achievements of each target.
RESULTS: About 7.2% achieved all treatment targets. Of these, 35.1% reached systolic and diastolic (46.7%) BP targets. About 60.2% and 28.2% achieved optimal LDL-C and HbA1c, respectively. Working participants had lower odds of having optimal systolic (aOR = 0.34, 95% CI: 0.13-0.90) and diastolic (aOR = 0.41, 95% CI: 0.17-0.96) BP. Those who adhered to treatments were more likely to achieve LDL-C and HbA1c targets; (aOR = 1.72, 95% CI: 1.10-2.69) and (aOR = 2.46, 95% CI: 1.25-4.83), respectively.
CONCLUSIONS: The control of risk factors among high CV risk patients in this study was suboptimal. Urgent measures such as improving medication adherence are warranted.
METHODS: We used data spanning 2010-2018 from children aged 2-12 years within the Chicago Area Patient-Centered Outcomes Research Network-an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample.
RESULTS: Using data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample.
CONCLUSIONS: Stratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.
METHODS: We followed the Joanna Briggs Institute guideline for the conduct of this scoping review. We searched MEDLINE, Embase, LILACS and study registers from inception to 14 March 2022. We included cross-sectional and cohort studies in populations representing a geographically-defined unit (urban or rural) in LMICs, and with data on CVH metrics i.e. all health or clinical factors (cholesterol, blood pressure, glycemia and body mass index) and at least one health behavior (smoking, diet or physical activity). We report findings following the PRISMA-Scr extension for scoping reviews.
RESULTS: We included 251 studies; 85% were cross-sectional. Most studies (70.9%) came from just ten countries. Only 6.8% included children younger than 12 years old. Only 34.7% reported seven metrics; 25.1%, six. Health behaviors were mostly self-reported; 45.0% of studies assessed diet, 58.6% physical activity, and 90.0% smoking status.
CONCLUSIONS: We identified a substantial and heterogeneous body of research presenting CVH metrics in LMICs. Few studies assessed all components of CVH, especially in children and in low-income settings. This review will facilitate the design of future studies to bridge the evidence gap. This scoping review protocol was previously registered on OSF: https://osf.io/sajnh.