Risk factors and management of pulmonary infection in elderly patients with heart failure: A retrospective analysis

Developing a prediction model for preoperative acute heart failure in elderly hip fracture patients: a retrospective analysis

BACKGROUND

Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis.

OBJECTIVE

This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model.

METHODS

This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model's calibration.

RESULTS

Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381-0.734, P < 0.001), Age (OR = 1.760, 95% CI: 1.251-2.479, P = 0.001), Coronary Heart Disease (OR = 1.977, 95% CI: 1.454-2.687, P < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95% CI: 1.154-5.346, P = 0.020), Complications (OR = 1.516, 95% CI: 1.033-2.226, P = 0.033), Anemia (OR = 2.668, 95% CI: 1.850-3.847, P < 0.001), and Hypoalbuminemia (OR 2.442, 95% CI: 1.682-3.544, P < 0.001). The linear prediction model of acute heart failure was Logit(P) = -2.167-0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia, and the nomogram prediction model was established. The AUC of the predictive model was 0.763, indicating good predictive performance. Decision curve analysis revealed that the prediction model offers the greatest net benefit when the threshold probability ranges from 4 to 62%.

CONCLUSION

The prediction model we developed exhibits excellent accuracy in predicting the onset of acute heart failure preoperatively in elderly patients with hip fractures. It could potentially serve as an effective and useful clinical tool for physicians in conducting clinical assessments and individualized treatments.

Efficacy of Sacubitril Valsartan sodium tablets in patients with heart failure combined with pulmonary infection and long-term recurrence rate

OBJECTIVE

To observe the therapeutic effect of Sacubitril Valsartan sodium tablets (SVST) on heart failure (HF) complicated by pulmonary infection (PI), and to provide a reference for future medication.

METHODS

A total of 89 patients with HF complicated by PI who were treated at Dongying People's Hospital from January 2019 to May 2020 were selected as study subjects in this retrospective study. The control group consisted of 41 patients who received conventional treatment, while the study group included 48 patients who received SVST in addition to conventional treatment. The time to disappearance/improvement of chest tightness, shortness of breath, cough, and moist rales in both groups were recorded. The levels of brain natriuretic peptide (BNP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and procalcitonin (PCT) were measured before and after treatment. Changes in cardiac function were observed, and the Clinical Pulmonary Infection Score (CPIS) and Sequential Organ Failure Assessment (SOFA) were used to assess PI. The clinical efficacy and adverse reactions were evaluated after treatment. Follow-up lasted 2 years, during which the readmission rate due to HF and mortality rate were calculated.

RESULTS

Patients in the study group experienced a shorter time to disappearance/improvement of chest tightness, shortness of breath, cough, and moist rales compared to the control group (all P<0.05). The study group also showed reduced levels of BNP, IL-6, TNF-α, and PCT, as well as lower CPIS and SOFA scores after treatment (all P<0.05), with significantly improved cardiac function (P<0.05). Additionally, the total effective rate was higher in the study group than in the control group (P<0.05), and there was no significant difference in adverse reactions between the two groups (P>0.05). Follow-up revealed no difference in mortality between the two groups (P>0.05), but the study group had a lower readmission rate (P<0.05).

CONCLUSION

SVST is effective in treating HF complicated by PI, ensures a good prognosis for patients, and is recommended for clinical use.

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients

Objective

To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model.

Methods

Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve.

Results

A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value.

Conclusions

Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value.

Viral coinfection in hospitalized patients during the COVID-19 pandemic in Southern Brazil: a retrospective cohort study

PURPOSE

Since the worldwide spread of SARS-CoV-2, different strategies have been followed to combat the pandemic and limit virus transmission. In the meantime, other respiratory viruses continued to circulate, though at decreased rates.

METHODS

This study was conducted between June and July 2022, in a hospital in the metropolitan region of Rio Grande do Sul state, in the southernmost state of Brazil. The 337 hospitalized patients included those with respiratory symptoms without delimitation of age. Reverse transcription-quantitative real-time polymerase chain reaction detected 15 different respiratory viruses and confirmed coinfections in the samples. Different statistical tests were applied to evaluate the association between associations of clinical characteristics and coinfection.

RESULTS

Sampling corresponds to 337 selected and 330 patients analyzed. The principal clinical outcome found was hospital discharge in 309 (94%) cases, while 21 (6%) resulted in death. The principal viral agents related to coinfections were Human rhinovirus, Human enterovirus, and Respiratory syncytial virus. The most frequent viral agent detected was SARS-CoV-2, with 60 (18%) infections, followed by 51 (15%) cases of Respiratory syncytial virus B (15%) and 44 (13%) cases of Human rhinovirus 1. Coinfection was mainly observed in children, while adults and the elderly were more affected by a single infection. Analyzing COVID-19 vaccination, 175 (53%) were unvaccinated while the remainder had at least one dose of the vaccine.

CONCLUSIONS

This study presents information to update the understanding of viral circulation in the region. Furthermore, the findings clarify the behavior of viral infections and possible coinfections in hospitalized patients, considering different ages and clinical profiles. In addition, this knowledge can help to monitor the population's clinical manifestations and prevent future outbreaks of respiratory viruses.

Comparison of quality/quantity mNGS and usual mNGS for pathogen detection in suspected pulmonary infections

Improved metagenomic next-generation sequencing (mNGS), for example, quality/quantity mNGS (QmNGS), is being used in the diagnosis of pulmonary pathogens. There are differences between QmNGS and the usual mNGS (UmNGS), but reports that compare their detection performances are rare. In this prospective study of patients enrolled between December 2021 and March 2022, the bronchoalveolar lavage fluid of thirty-six patients with suspected pulmonary infection was assessed using UmNGS and QmNGS. The sensitivity of QmNGS was similar to that of UmNGS. The specificity of QmNGS was higher than that of UmNGS; however, the difference was not statistically significant. The positive likelihood ratios (+LR) of QmNGS and UmNGS were 3.956 and 1.394, respectively, and the negative likelihood ratios (-LR) were 0.342 and 0.527, respectively. For the co-detection of pathogens, the depth and coverage of the QmNGS sequencing were lower than those of UmNGS, while for the detection of pathogens isolated from patients with pulmonary infection, the concordance rate was 77.2%. In the eleven patients with nonpulmonary infection, only viruses were detected using QmNGS, while UmNGS detected not only viruses but also bacteria and fungi. This study provides a basis for the selection of mNGS for the diagnosis of suspected pulmonary infection.

A Prognostic Model Based on Clinical Biomarkers for Heart Failure in Adult Patients Following Allogeneic Hematopoietic Stem Cell Transplantation

Heart failure (HF) is an uncommon but serious cardiovascular complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Unfortunately, knowledge about early mortality prognostic factors in patients with HF after allo-HSCT is limited, and an easy-to-use prognostic model is not available. This study aimed to develop and validate a clinical-biomarker prognostic model capable of predicting HF mortality following allo-HSCT that uses a combination of variables readily available in clinical practice. To investigate this issue, we conducted a retrospective analysis at our center with 154 HF patients who underwent allo-HSCT between 2008 and 2021. The patients were separated according to the time of transplantation, with 100 patients composing the derivation cohort and the other 54 patients composing the external validation cohort. We first calculated the univariable association for each variable with 2-month mortality in the derivation cohort. We then included the variables with a P value <.1 in univariate analysis as candidate predictors in the multivariate analysis using a backward stepwise logistic regression model. Variables remaining in the final model were identified as independent prognostic factors. To predict the prognosis of HF, a scoring system was established, and scores were assigned to the prognostic factors based on the regression coefficient. Finally, 4 strongly significant independent prognostic factors for 2-month mortality from HF were identified using multivariable logistic regression methods with stepwise variable selection: pulmonary infection (P = .005), grade III to IV acute graft-versus-host disease (severe aGVHD; P = .033), lactate dehydrogenase (LDH) >426 U/L (P = .049), and brain natriuretic peptide (BNP) >1799 pg/mL (P = .026). A risk grading model termed the BLIPS score (for BNP, LDH, cardiac troponin I, pulmonary infection, and severe aGVHD) was constructed according to the regression coefficients. The validated internal C-statistic was .870 (95% confidence interval [CI], .798 to .942), and the external C-statistic was .882 (95% CI, .791-.973). According to the calibration plots, the model-predicted probability correlated well with the actual observed frequencies. The clinical use of the prognostic model, according to decision curve analysis, could benefit HF patients. The BLIPS model in our study can serve to identify HF patients at higher risk for mortality early, which might aid designing timely targeted therapies and eventually improving patients' survival and prognosis.

Diagnostic value of echocardiography combined with serum C-reactive protein level in chronic heart failure

BACKGROUND

Chronic heart failure (CHF) is regarded as common clinical heart disease. This study aims to investigate the clinical diagnostic value of echocardiography (Echo) and serum C-reactive protein (CRP) levels in patients with CHF.

METHODS

A total of 75 patients with CHF (42 males, 33 females, age 62.72 ± 1.06 years) were enrolled as study subjects, with 70 non-CHF subjects (38 males, 32 females, age 62.44 ± 1.28 years) as controls. The left ventricular ejection fraction (LVEF), fraction shortening rate of the left ventricle (FS), and early to late diastolic filling (E/A) were determined by Echo, followed by an examination of the expression of serum CRP by ELISA. In addition, the Pearson method was used to analyze the correlation between echocardiographic quantitative parameters (EQPs) (LVEF, FS, and E/A) and serum CRP levels. Receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic efficacy of EQPs and serum CRP levels for CHF. The independent risk factors for CHF patients were measured by logistics regression analysis.

RESULTS

The serum CRP level of CHF patients was elevated, the values of LVEF and FS decreased, and the E/A values increased. ROC curve revealed that the EQPs (LVEF, FS, and E/A) combined with serum CRP had high diagnostic values for CHF patients. Logistic regression analysis showed that the EQPs (LVEF, FS, and E/A) and serum CRP levels were independent risk factors for CHF patients.

CONCLUSION

Echo combined with serum CRP level has high clinical diagnostic values for CHF patients.