The Significance of Hematologic Indices in Patients with Heart Failure
Bibliographic record
Abstract
The leading cause of death worldwide is coronary heart disease (CHD). In patients with chronic heart failure, hemographic markers have been associated with clinical outcomes. Hemographic indices, or measures of white blood cells, are easy and useful, clear and simple predictors of both subclinical and systemic inflammation. Methods:A retrospective study was conducted on the hospitalized patient for HF from April 2019- May 2020. Total 170 patients were enrolled in current study. The inclusion criteria of the study was patients above 18 years, hospitalized patient for HF, having an EF< 40% and having two or more symptoms of HF as well as be discharged alive from the hospital. The hemograhic indices were taken for each patient at the time of admission. The hemographic indices were defined as WBC count, neutrophil count, RL, NLR, and PLR. The neutrophil-platelet ratio (NLR) and the platelet-lymphocyte ratio (PLR) were derived as the ratios of neutrophil and platelet counts to lymphocyte counts, respectively. The entire test was performed by the hospital biochemistry lab under standard protocol. Patients were followed till six months. At the time of admission complete medical history and hemographic indices was recorded. Data was entered and analyzed using SPSS 25.0. The quantitative variables were presented by mean and standard deviation and qualitative with frequency/percentages. All the qualitative variables were compared among both by using Chi-square test and all quantitative variables by independent sample t test. The p-value less than 5% were considered as significant. Results:Total 170 patients were enrolled in current study among which 123(72.3%) survived and 47(27.64%) were died during 6 month follow up. The mean age among survived were 57.55+7.5 and dead was 59.96+7.9 (P-value= 0.06). According to gender male have dominance, in survived patients there were 98(79.7%) and in dead 38(80.9%) males (P-value= 0.86). 53(43.1%) patients were smokers in survived patients and 27(57.4%) in dead (P-value= 0.09). Status of comorbidities showed that the diabetes mellitus showed quite common among groups. The neutrophil count, WBC, lymphocyte count and NLR were elevated in deceased patients. The Platelet counts and hemoglobin levels were low in deceased. Conclusion: It was concluded from current study that the increased neutrophil count, WBC, lymphocyte count and NLR were associated with the mortality of HF patients
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".