Trends of Hemoglobin Oximetry: Do They Help Predict Blood Transfusion During Trauma Patient Resuscitation?

BACKGROUND: A noninvasive decision support tool for emergency transfusion would benefit triage and resuscitation. We tested whether 15 minutes of continuous pulse oximetry-derived hemoglobin measurements (SpHb) predict emergency blood transfusion better than conventional oximetry, vital signs, and invasive point-of-admission (POA) laboratory testing. We hypothesized that the trends in noninvasive SpHb features monitored for 15 minutes predict emergency transfusion better than pulse oximetry, shock index (SI = heart rate/systolic blood pressure), or routine POA laboratory measures. METHODS: We enrolled direct trauma patient admissions ≥18 years with prehospital SI ≥0.62, collected vital signs (continuous SpHb and conventional pulse oximetry, heart rate, and blood pressure) for 15 minutes after admission, and recorded transfusion (packed red blood cells [pRBCs]) within 1 to 3, 1 to 6, and 1 to 12 hours of admission. One blood sample was drawn during the first 15 minutes. The laboratory Hb was compared with its corresponding SpHb reading for numerical, clinical, and prediction difference. Ten prediction models for transfusion, including combinations of prehospital vital signs, SpHb, conventional oximetry, and routine POA, were selected by stepwise logistic regression. Predictions were compared via area under the receiver operating characteristic curve by the DeLong method. RESULTS: A total of 677 trauma patients were enrolled in the study. The prediction performance of the models, including POA laboratory values and SI (and the need for blood pressure), was better than those without POA values or SI. In predicting pRBC 1- to 3-hour transfusion, adding SpHb features (receiver operating characteristic curve [ROC] = 0.65; 95% confidence interval [CI], 0.53-0.77) does not improve ROC from the base model (ROC = 0.64; 95% CI, 0.52-0.76) with P = 0.48. Adding POA laboratory Hb features (ROC = 0.72; 95% CI, 0.60-0.84) also does not improve prediction performance (P = 0.18). Other POA laboratory testing predicted emergency blood use with ROC of 0.88 (95% CI, 0.81-0.96), significantly better than the use of SpHb (P = 0.00084) and laboratory Hb (P = 0.0068). CONCLUSIONS: SpHb added no benefit over conventional oximetry to predict urgent pRBC transfusion for trauma patients. Both models containing POA laboratory test features performed better at predicting pRBC use than prehospital SI, the current best noninvasive vital signs transfusion predictor.

Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values.

BACKGROUND: Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone. METHODS: Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR] / systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong's method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2 saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models. RESULTS: We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88; p < 0.001). An algorithm including triage vital signs, pulse oximetry features, and laboratory values improved accuracy of transfusion prediction (3-hour AUROC, 0.84; p < 0.001) (massive transfusion AUROC, 0.91; p < 0.001). CONCLUSION: Automated analysis of triage vital signs, 15 minutes of pulse oximetry signals, and laboratory values predicted use of blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion. LEVEL OF EVIDENCE: Epidemiologic/prognostic study, level III.

Assessing trauma care provider judgement in the prediction of need for life-saving interventions.

INTRODUCTION: Human judgement on the need for life-saving interventions (LSI) in trauma is poorly studied, especially during initial casualty management. We prospectively examined early clinical judgement and compared clinical experts' predictions of LSI to their later occurrence. PATIENTS AND METHODS: Within 10-15 min of direct trauma admission, we surveyed the predictions of pre-hospital care providers (PHP, 92% paramedics), trauma centre nurses (RN), and attending or fellow trauma physicians (MD) on the need for LSI. The actual outcomes including fluid bolus, intubation, transfusion (<1h and 1-6h), and emergent surgical interventions were observed. Cohen's kappa statistic (K) and percentage agreement were used to measure agreement among provider responses. Sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were calculated to compare clinical judgement to actual patient interventions. RESULTS: Among 325 eligible trauma patient admissions, 209 clinical judgement of LSIs were obtained from all three providers. Cohen's kappa statistic for agreement between pairs of provider groups demonstrated no "disagreement" (K<0) between groups, "fair" agreement for fluid bolus (K=0.12-0.19) and blood transfusion 0-6h (K=0.22-0.39), and "moderate" (K=0.45-0.49) agreement between PHP and RN regarding intubation and surgical interventions, but no "excellent" (K ≥ 0.81) agreement between any pair of provider groups for any intervention. The percentage agreement across the different clinician groups ranged from 50% to 83%. NPV was 90-99% across providers for all interventions except fluid bolus. CONCLUSIONS: Expert clinical judgement provides a benchmark for the prediction of major LSI use in unstable trauma patients. No excellent agreement exists across providers on LSI predictions. It is possible that quality improvement measures and computer modelling-based decision-support could reduce errors of LSI commission and omission found in resuscitation at major trauma centres and enhance decision-making in austere trauma settings by less well-trained providers than those surveyed.

Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.

Early recognition of hemorrhage during the initial resuscitation of injured patients is associated with improved survival in both civilian and military casualties. We tested a transfusion and lifesaving intervention (LSI) prediction algorithm in comparison with clinical judgment of expert trauma care providers. We collected 15 min of pulse oximeter photopletysmograph waveforms and extracted features to predict LSIs. We compared this with clinical judgment of LSIs by individual categories of prehospital providers, nurses, and physicians and a combined judgment of all three providers using the Area Under Receiver Operating Curve (AUROC). We obtained clinical judgment of need for LSI from 405 expert clinicians in135 trauma patients. The pulse oximeter algorithm predicted transfusion within 6 h (AUROC, 0.92; P < 0.003) more accurately than either physicians or prehospital providers and as accurately as nurses (AUROC, 0.76; P = 0.07). For prediction of surgical procedures, the algorithm was as accurate as the three categories of clinicians. For prediction of fluid bolus, the diagnostic algorithm (AUROC, 0.9) was significantly more accurate than prehospital providers (AUROC, 0.62; P = 0.02) and nurses (AUROC, 0.57; P = 0.04) and as accurate as physicians (AUROC, 0.71; P = 0.06). Prediction of intubation by the algorithm (AUROC, 0.92) was as accurate as each of the three categories of clinicians. The algorithm was more accurate (P < 0.03) for blood and fluid prediction than the combined clinical judgment of all three providers but no different from the clinicians in the prediction of surgery (P = 0.7) or intubation (P = 0.8). Automated analysis of 15 min of pulse oximeter waveforms predicts the need for LSIs during initial trauma resuscitation as accurately as judgment of expert trauma clinicians. For prediction of emergency transfusion and fluid bolus, pulse oximetry features were more accurate than these experts. Such automated decision support could assist resuscitation decisions, trauma team, and operating room and blood bank preparations.

Contraceptive practice, unwanted pregnancies and induced abortion in Southwest Nigeria.

Despite widespread awareness of and access to modern contraception, high rates of unwanted pregnancies and abortions still persist in many parts of the world, even where abortion is legally restricted. This article explores perspectives on contraception and abortion, contraceptive decision-making within relationships, and the management of unplanned pregnancies. It presents findings from an exploratory qualitative study based on 17 in-depth interviews and 6 focus group discussions conducted in 2 locations in Nigeria in 2006. The results suggest that couples do not practice contraception consistently because of perceived side effects and partner objections. Abortion is usually resorted to because pregnancy was unwanted due to incomplete educational attainment, economic hardship, immaturity, close pregnancy interval, and social stigma. Males usually have greater influence in contraceptive-decision making than females. Though induced abortion is negatively viewed in the community, it is still common, and women usually patronise quacks to obtain such services. An abortion experience can change future views and decisions towards contraception. Family planning interventions should include access to and availability of adequate family planning information. Educational campaigns should target males since they play an important role in contraceptive decision-making.